Enterprise social software (also known as or regarded as a major component of Enterprise 2.0), comprises social software as used in "enterprise" (business/commercial) contexts. It includes social and networked modifications to corporate intranets and other classic software platforms used by large companies to organize their communication. In contrast to traditional enterprise software, which imposes structure prior to use, enterprise social software tends to encourage use prior to providing structure. Carl Frappaolo and Dan Keldsen defined Enterprise 2.0 in a report written for Association for Information and Image Management (AIIM) as "a system of web-based technologies that provide rapid and agile collaboration, information sharing, emergence and integration capabilities in the extended enterprise". == Applications == === Functionality === Social software for an enterprise must (according to Andrew McAfee, Associate Professor, Harvard Business School) have the following functionality to work well: Search: allowing users to search for other users or content Links: grouping similar users or content together Authoring: including blogs and wikis Tags: allowing users to tag content Extensions: recommendations of users; or content based on profile Signals: allowing people to subscribe to users or content with RSS feeds McAfee recommends installing easy-to-use software which does not impose any rigid structure on users. He envisages an informal roll-out, but on a common platform to enable future collaboration between areas. He also recommends strong and visible managerial support to achieve this. In 2007 Dion Hinchcliffe expanded the list above by adding the following four functions: Freeform function: no barriers to authorship (meaning free from a learning curve or from restrictions) Network-oriented function, requiring web-addressable content in all cases Social function: stressing transparency (to access), diversity (in content and community members) and openness (to structure) Emergence function: requiring the provision of approaches that detect and leverage the collective wisdom of the community Enterprise search differs from a typical web search in its focus on "use within an organization by employees seeking information held internally, in a variety of formats and locations, including databases, document management systems, and other repositories". === Criticism === There has been recent criticism that the adaptation of the social paradigm (e.g. openness and altruistic behavior) does not always work well for the enterprise setting, which led some authors to question the proper functioning of enterprise social software. The findings from a novel study suggests that free and non-anonymous sharing of trusted information (beyond marketing or product information) is significantly influenced by concerns from business users.
ClearForest
ClearForest was an Israeli software company that developed and marketed text analytics and text mining solutions. == History == Founded in 1998, ClearForest had its headquarters just outside Boston and a development center in Or Yehuda. The company was acquired by Reuters in April, 2007. It now markets its services under the names Calais, OpenCalais, and OneCalais. ClearForest was previously venture-backed; its last funding round was led by Greylock Ventures and closed in 2005. Other investors included DB Capital Partners, Pitango, Walden Israel, Booz Allen, JP Morgan Partners and HarbourVest Partners. On February 7, 2008 Reuters announced the launch of Open Calais, a named-entity recognition and semantic analysis service that uses ClearForest technology. On April 30, 2007, Reuters announced that it would acquire ClearForest. Sources estimate the acquisition to be for $25 Million. == Solutions and products == ClearForest offers several hosted solutions, including: OpenCalais, a free web service and open API (for commercial and non-commercial use) that performs named-entity recognition and enables automatic metadata generation using the ClearForest financial module. Semantic Web Services (SWS), an on-demand service that makes ClearForest's natural language processing tools available as a standard web service. A subset of ClearForest's capabilities is available via SWS at no cost. Gnosis, a free Firefox extension that uses SWS to analyze the content of a web page. Gnosis identifies named entities such as people, companies, organizations, geographies and products on the page being viewed. Gnosis also automatically processes pages from Wikipedia, providing additional links for people, geographies and other entities which were not explicitly linked within the subject article. Harvest, a real-time machine-readable news service that uses SWS to process a company's news and document feeds and return machine-readable information about people, companies, locations and over 200 other entities facts and events. ClearForest also offers Text Analytics solutions targeted at specific business problems, including: Equity valuation for hedge funds and alternative investments firms Metadata & database creation for publishers and information providers/services Tapping "voice of customer" for market and survey research firms Quality Early Warning for vehicle, capital equipment & durable goods manufacturers
Stripe, Inc.
Stripe, Inc. is an Irish and American multinational financial services and software as a service (SaaS) company dual-headquartered in South San Francisco, California, United States, and Dublin, Ireland. The company primarily offers payment-processing software and application programming interfaces for e-commerce websites and mobile applications. Stripe is the largest privately owned financial technology company with a valuation of about $159 billion and over $1.9 trillion in payment volume processed in 2025, processing transactions for 5 million businesses in that year. == History == Irish entrepreneur brothers John and Patrick Collison founded Stripe in Palo Alto, California, in 2010, and serve as the company's president and CEO, respectively. In 2011 the company received a $2 million investment, including contributions from Elon Musk, PayPal founder Peter Thiel, Irish entrepreneur Liam Casey, and venture capital firms Sequoia Capital, Andreessen Horowitz, and SV Angel. In March 2013, Stripe made its first acquisition, Kickoff, a chat and task-management application. In 2012 the company moved from Palo Alto to San Francisco. In October 2019, the company announced that it would be moving from the South of Market area to Oyster Point in the neighbouring city of South San Francisco in 2021. In February 2021, Mark Carney, former governor of the Bank of Canada and of the Bank of England, was appointed to the company's board. Carney stepped down from his role with the company in 2025 in order to run for the leadership of the Liberal Party. Stripe acquired accountancy platform Recko in October 2021 whose solution was to be added to Stripe's existing suite of financial tools. In January 2022, Stripe entered a five-year partnership with Ford Motor Company. Through the deal, Stripe would handle transactions for consumer vehicle orders and reservations. That same month, Stripe partnered with Spotify to help the company monetize subscriptions. In April 2022, Twitter announced that it would partner with Stripe, Inc. (digital payments processor) for piloting cryptocurrency pay-outs for limited users in the platform. In April 2022, Stripe announced its strategic partnership with UK-based financial technology company ION. The Wall Street Journal reported in July 2022 that the company's internal share price had fallen, causing its implied valuation to drop from $95 billion to $74 billion. In November 2022, the company announced it intended to initiate layoffs, terminating some 14% of its workforce. Throughout 2022 and 2023, the company announced a number of large enterprise customers, including Airbnb, Amazon, Microsoft, Uber, BMW, Maersk, Zara, Lotus, Alaska Airlines, Le Monde, and Toyota. The company also announced in March 2023 that OpenAI is working with Stripe to commercialize its generative AI technology. In January 2025, Stripe sent layoff notices to nearly 300 workers, primarily affecting roles in Product, Operations and Engineering. The company experienced controversy when the company sent a cartoon picture of a duck to the laid-off employees. Stripe's Chief People Officer Rob McIntosh later apologized for the mistake. After re-enabling cryptocurrency pay-ins in April 2024, starting with USDC, Stripe completed the acquisition of Bridge in February 2025. The acquisition of the two-year-old stablecoin platform company is valued at $1.1 billion. In June 2025, the company acquired Privy, which powers crypto wallets. In September 2025, Stripe announced it was powering Instant Checkout in ChatGPT and released Agentic Commerce Protocol for agentic commerce, which was co-developed with OpenAI. In October 2025, the company opened its second headquarters in Dublin, Ireland. In February 2026, Stripe was valued at $159 billion in a tender offer posted for employees and shareholders. The tender offer was about a 70% increase from Stripe's previous valuation published in February 2025, where it was valued at $91.5 billion. Stripe also announced that its total volume increased to $1.9 trillion USD in 2025, a 34% increase from 2024. == Technology company == === Payment processing === Stripe provides application programming interfaces that web developers can use to integrate payment processing into their websites and mobile applications. The company introduced Stripe Connect in 2012, a multiparty payments solution that lets software developers embed payments natively into their products. In April 2018, Stripe released antifraud tools, branded "Radar", that block fraudulent transactions. The same year, it expanded its services to include a billing product for online businesses, allowing businesses to manage subscription recurring revenue and invoicing. Stripe's point-of-sale service called Terminal was made available to US users on 11 June 2019. Terminal had previously been invitation-only. Terminal is currently available in Australia, Canada, France, Germany, Ireland, the Netherlands, New Zealand, Singapore, and the United Kingdom. The service offers physical credit-card readers designed to work with Stripe. On 5 September 2019, Stripe launched a merchant cash-advance scheme called Stripe Capital. The scheme allows Stripe merchants to request an advance on future payments they expect to process through their Stripe merchant account. In June 2021, the company launched Stripe Tax, a service to allow businesses to automatically calculate and collect sales tax, VAT, and GST, initially rolling out to 30 countries and all US states. As of 2025, it has been made available in 102 countries. In May that year, Stripe introduced Payment Links, a no-code product allowing businesses to create a link to a checkout page and begin accepting payments on social platforms or direct channels. In January 2022, Stripe agreed to acquire Terminal manufacturing partner BBPOS, allowing the company to bring the hardware development of Terminal readers in-house. In February, it was announced as Apple's first partner on in-person Tap to Pay, which enables businesses to accept contactless payments using an iPhone and a partner-enabled iOS app. In May, Stripe announced Data Pipeline, a tool for Stripe users who store data with Amazon Redshift or Snowflake Data Cloud. Data Pipeline syncs Stripe data and reports with Amazon Redshift or Snowflake Data Cloud, where they can be queried in combination with other business information. That month, the company also introduced Stripe Financial Connections, enabling businesses to establish direct connections with their customers’ bank accounts to verify accounts for payments and pay-outs, check balances to reduce payment failures, and cut fraud by confirming bank account ownership. In September 2023, Stripe announced that its optimized checkout suite allowed businesses to offer their customers more than 100 payment methods. In May 2025, Stripe announced a new AI foundational model for payments, and introduced stablecoin powered accounts. === Corporate finance === In July 2018, Stripe introduced Stripe Issuing, a product that allows online businesses and platforms to create their own physical and digital credit and debit cards. === Atlas === On 14 February 2016, the company launched the Atlas platform to help start-ups register as US corporations, targeting foreign entrepreneurs. The platform was originally invitation-only. In March 2016, Cuba was added to the list of countries covered under the program. Originally, companies registered using Atlas were set up as Delaware-based C corporations. As of 30 April 2018, the option to be registered as limited liability companies was added. Companies set up using Atlas automatically had a business bank account and Stripe merchant account set up. === Link === In May 2021, Stripe launched Link, a service for saving and auto-filling payment details when paying via Stripe. The service supported payments in over 185 countries and Stripe reported plans to make it available to platform businesses through its API. In September 2025, Patrick Collison announced that Link had surpassed 200 million users. === Other === In 2018, Stripe started a publishing company named Stripe Press to promote ideas that support businesses. In 2019, Stripe began offering loans and credit cards to businesses in the United States. The company stated that loans are approved automatically using machine-learning models, with no human intervention. The following year, the company introduced Stripe Treasury, which provides its platform users APIs to embed financial services, allowing their customers to send, receive, and store funds. In October 2020, Stripe announced Stripe Climate, a service for businesses to fund atmospheric carbon research and capture. In 2022, Stripe started a new subsidiary called Frontier that would direct spending on carbon removal. It announced $925 million in funding from major Silicon Valley companies to fund start up companies performing carbon capture to kick-start the industry. Stripe Identity, launched in Ju
Private cloud computing infrastructure
Private cloud computing infrastructure is a category of cloud computing that provides comparable benefits to public cloud systems, such as self-service and scalability, but it does so via a proprietary framework. In contrast to public clouds, which cater to multiple entities, a private cloud is specifically designed for the requirements and objectives of one organization. == Definition == A private cloud computing infrastructure constitutes a distinctive model of cloud computing that facilitates a secure and distinct cloud environment where only the intended client can function. It can either be physically housed in the organization's in-house data center or be managed by a third-party provider. In a private cloud, the infrastructure and services are always sustained on a private network, and both the hardware and software are devoted exclusively to a single organization. == History == The concept of private cloud infrastructure started to take shape around the mid-2000s, coinciding with the rise of other cloud computing forms. It came into existence as a solution to the shortcomings of public clouds, particularly concerns over data control, security, and network performance. IT departments began to mirror the automation and self-service features of the public cloud in their data centers. Over time, these services became more advanced, and private cloud technology has been refined to address businesses and organizations' diverse needs. == Architecture == Private cloud computing infrastructure generally involves a mix of hardware, network infrastructure, and virtualization software. The hardware, often referred to as a cloud server or cloud array, consists of a server rack or a collection of server racks containing the storage and processors that constitute the cloud. The virtualization software, such as Hyper-V, OpenStack, or VMWare, establishes and oversees virtual machines with which users interact. The network infrastructure connects the private cloud to users and may facilitate connectivity with other on-premises data centers or clouds. == Applications == Private cloud infrastructures are usually utilized by medium to large businesses and organizations that need robust control over their data, have extensive computing needs, or have specific regulatory or compliance obligations. This includes healthcare organizations, government agencies, financial institutions, and any business that needs to process and store large data volumes.
Gold (linker)
In software engineering, gold is a linker for ELF files. It became an official GNU package and was added to binutils in March 2008 and first released in binutils version 2.19. gold was developed by Ian Lance Taylor and a small team at Google. The motivation for writing gold was to make a linker that is faster than the GNU linker, especially for large applications coded in C++. Unlike the GNU linker, gold does not use the BFD library to process object files. While this limits the object file formats it can process to ELF only, it is also claimed to result in a cleaner and faster implementation without an additional abstraction layer. The author cited complete removal of BFD as a reason to create a new linker from scratch rather than incrementally improve the GNU linker. This rewrite also fixes some bugs in old ld that break ELF files in various minor ways. To specify gold in a makefile, one sets the LD or LD environment variable to ld.gold. To specify gold through a compiler option, one can use the gcc option -fuse-ld=gold. Fedora has moved gold from binutils into its own package due to concerns it is suffering from bitrot after Google's interest has moved to LLVM. In particular, gold does not read LDFLAGS variable, so cannot see libraries in folders like /usr/local/lib. On 2025-02-02 the 2.44 version of GNU Binutils removed gold from the default source distribution and into a separate package, stating that "the gold linker is now deprecated and will eventually be removed unless volunteers step forward and offer to continue development and maintenance".
Circle Hough Transform
The circle Hough Transform (CHT) is a basic feature extraction technique used in digital image processing for detecting circles in imperfect images. The circle candidates are produced by “voting” in the Hough parameter space and then selecting local maxima in an accumulator matrix. It is a specialization of the Hough transform. == Theory == In a two-dimensional space, a circle can be described by: ( x − a ) 2 + ( y − b ) 2 = r 2 ( 1 ) {\displaystyle \left(x-a\right)^{2}+\left(y-b\right)^{2}=r^{2}\ \ \ \ \ (1)} where (a,b) is the center of the circle, and r is the radius. If a 2D point (x,y) is fixed, then the parameters can be found according to (1). The parameter space would be three dimensional, (a, b, r). And all the parameters that satisfy (x, y) would lie on the surface of an inverted right-angled cone whose apex is at (x, y, 0). In the 3D space, the circle parameters can be identified by the intersection of many conic surfaces that are defined by points on the 2D circle. This process can be divided into two stages. The first stage is fixing radius then find the optimal center of circles in a 2D parameter space. The second stage is to find the optimal radius in a one dimensional parameter space. === Find parameters with known radius R === If the radius is fixed, then the parameter space would be reduced to 2D (the position of the circle center). For each point (x, y) on the original circle, it can define a circle centered at (x, y) with radius R according to (1). The intersection point of all such circles in the parameter space would be corresponding to the center point of the original circle. Consider 4 points on a circle in the original image (left). The circle Hough transform is shown in the right. Note that the radius is assumed to be known. For each (x,y) of the four points (white points) in the original image, it can define a circle in the Hough parameter space centered at (x, y) with radius r. An accumulator matrix is used for tracking the intersection point. In the parameter space, the voting number of those points that have a newly defined circle passing through them would be increased by one for every circle. Then the local maxima point (the red point in the center in the right figure) can be found. The position (a, b) of the maxima would be the center of the original circle. === Multiple circles with known radius R === Multiple circles with same radius can be found with the same technique. Note that, in the accumulator matrix (right fig), there would be at least 3 local maxima points. === Accumulator matrix and voting === In practice, an accumulator matrix is introduced to find the intersection point in the parameter space. First, we need to divide the parameter space into “buckets” using a grid and produce an accumulator matrix according to the grid. The element in the accumulator matrix denotes the number of “circles” in the parameter space that are passing through the corresponding grid cell in the parameter space. The number is also called “voting number”. Initially, every element in the matrix is zeros. Then for each “edge” point in the original space, we can formulate a circle in the parameter space and increase the voting number of the grid cell which the circle passes through. This process is called “voting”. After voting, we can find local maxima in the accumulator matrix. The positions of the local maxima are corresponding to the circle centers in the original space. === Find circle parameter with unknown radius === Since the parameter space is 3D, the accumulator matrix would be 3D, too. We can iterate through possible radii; for each radius, we use the previous technique. Finally, find the local maxima in the 3D accumulator matrix. Accumulator array should be A[x,y,r] in the 3D space. Voting should be for each pixels, radius and theta A[x,y,r] += 1 The algorithm : For each A[a,b,r] = 0; Process the filtering algorithm on image Gaussian Blurring, convert the image to grayscale ( grayScaling), make Canny operator, The Canny operator gives the edges on image. Vote on all possible circles in accumulator. The local maximum voted circles of Accumulator A gives the circle Hough space. The maximum voted circle of Accumulator gives the circle. The Incrementing for Best Candidate : For each A[a,b,r] = 0; // fill with zeroes initially, instantiate 3D matrix For each cell(x,y) For each theta t = 0 to 360 // the possible theta 0 to 360 b = y – r sin(t PI / 180); //polar coordinate for center (convert to radians) a = x – r cos(t PI / 180); //polar coordinate for center (convert to radians) A[a,b,r] +=1; //voting end end == Examples == === Find circles in a shoe-print === The original picture (right) is first turned into a binary image (left) using a threshold and Gaussian filter. Then edges (mid) are found from it using canny edge detection. After this, all the edge points are used by the Circle Hough Transform to find underlying circle structure. == Limitations == Since the parameter space of the CHT is three dimensional, it may require lots of storage and computation. Choosing a bigger grid size can ameliorate this problem. However, choosing an appropriate grid size is difficult. Since too coarse a grid can lead to large values of the vote being obtained falsely because many quite different structures correspond to a single bucket. Too fine a grid can lead to structures not being found because votes resulting from tokens that are not exactly aligned end up in different buckets, and no bucket has a large vote. Also, the CHT is not very robust to noise. == Extensions == === Adaptive Hough Transform === J. Illingworth and J. Kittler introduced this method for implementing Hough Transform efficiently. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. This method is substantially superior to the standard Hough Transform implementation in both storage and computational requirements. == Application == === People Counting === Since the head would be similar to a circle in an image, CHT can be used for detecting heads in a picture, so as to count the number of persons in the image. === Brain Aneurysm Detection === Modified Hough Circle Transform (MHCT) is used on the image extracted from Digital Subtraction Angiogram (DSA) to detect and classify aneurysms type. == Implementation code == Circle Detection via Standard Hough Transform, by Amin Sarafraz, Mathworks (File Exchange) Hough Circle Transform, OpenCV-Python Tutorials (archived version on archive.org)
IgHome
igHome is a customizable start page introduced in 2012 as an alternative to iGoogle, the personal web portal launched by Google in May 2005. Just like iGoogle, igHome offers users the possibility to build a start page containing a central search box and a number of gadgets. igHome mimics the user interface of iGoogle. Registered igHome users can create multiple tabs and import RSS feeds.