AI Generator With Image

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

  • AsoSoft text corpus

    AsoSoft text corpus

    The AsoSoft text corpus is the first large-scale Kurdish text corpus, collected and processed by the AsoSoft research and development group. It contains 458,000 documents (188 million tokens) that are collected from sources such as websites, news agencies, books, and magazines. The corpus is partially tagged by topic, so it can be used for topic identification tasks. Also, it is applicable for extracting language model and computational lexicon information. Part of the corpus (75 million tokens) is available online for non-commercial use. The corpus uses the TEI format.

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  • Hamilton C shell

    Hamilton C shell

    Hamilton C shell is a clone of the Unix C shell and utilities for Microsoft Windows created by Nicole Hamilton at Hamilton Laboratories as a completely original work, not based on any prior code. It was first released on OS/2 on December 12, 1988 and on Windows NT in July 1992. The OS/2 version was discontinued in 2003 but the Windows version continues to be actively supported. == Design == Hamilton C shell differs from the Unix C shell in several respects. These include its compiler architecture, its use of threads, and the decision to follow Windows rather than Unix conventions. === Parser === The original C shell uses an ad hoc parser. This has led to complaints about its limitations. It works well enough for the kinds of things users type interactively but not very well for the more complex commands a user might take time to write in a script. It is not possible, for example, to pipe the output of a foreach statement into grep. There was a limit to how complex a command it could handle. By contrast, Hamilton uses a top-down recursive descent parser that allows it to compile statements to an internal form before running them. As a result, statements can be nested or piped arbitrarily. The language has also been extended with built-in and user-defined procedures, local variables, floating point and additional expression, editing and wildcarding operators, including an "indefinite directory" wildcard construct written as "..." that matches zero or more directory levels as required to make the rest of the pattern match. === Threads === Lacking fork or a high performance way to recreate that functionality, Hamilton uses the Windows threads facilities instead. When a new thread is created, it runs within the same process space and it shares all of the process state. If one thread changes the current directory or the contents of memory, it's changed for all the threads. It's much cheaper to create a thread than a process but there's no isolation between them. To recreate the missing isolation of separate processes, the threads cooperate to share resources using locks. === Windows conventions === Hamilton differs from other Unix shells in that it also directly supports Windows conventions for drive letters, filename slashes, escape characters, etc.

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  • Content Disarm and Reconstruction

    Content Disarm and Reconstruction

    Content Disarm and Reconstruction (CDR) is a computer security technology for removing potentially malicious code from files. Unlike malware analysis, CDR technology does not determine or detect malware's functionality but removes all file components that are not approved within the system's definitions and policies. It is used to prevent cyber security threats from entering a corporate network perimeter. Channels that CDR can be used to protect include email and website traffic. Advanced solutions can also provide similar protection on computer endpoints, or cloud email and file sharing services. There are three levels of CDR; 1) flattening and converting the original file to a PDF, 2) stripping active content while keeping the original file type, and 3) eliminating all file-borne risk while maintaining file type, integrity and active content. Beyond these three levels, there are also more advanced forms of CDR that is able to perform "soft conversion" and "hard conversion", based on the user's preference in balancing usability and security. == Applications == CDR works by processing all incoming files of an enterprise network, deconstructing them, and removing the elements that do not match the file type's standards or set policies. CDR technology then rebuilds the files into clean versions that can be sent on to end users as intended. Because CDR removes all potentially malicious code, it can be effective against zero-day vulnerabilities that rely on being an unknown threat that other security technologies would need to patch against to maintain protection. CDR can be used to prevent cyber threats from variety of sources: Email Data Diodes Web Browsers Endpoints File Servers FTP Cloud email or webmail programs SMB/CIFS Removable media scanning (CDR Kiosk) CDR can be applied to a variety of file formats including: Images Office documents PDF Audio/video file formats Archives HTML == Open source implementations == DocBleach ExeFilter

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  • Digital image

    Digital image

    A digital image is an image composed of picture elements, also known as pixels, each with finite, discrete quantities of numeric representation for its intensity or gray level that is an output from its two-dimensional functions fed as input by its spatial coordinates denoted with x, y on the x-axis and y-axis, respectively. An image can be vector or raster type. By itself, the term "digital image" usually refers to raster images or bitmapped images (as opposed to vector images). == Raster == Raster images have a finite set of digital values, called picture elements or pixels. The digital image contains a fixed number of rows and columns of pixels. Pixels are the smallest individual element in an image, holding quantized values that represent the brightness of a given color at any specific point. Typically, the pixels are stored in computer memory as a raster image or raster map, a two-dimensional array of small integers. These values are often transmitted or stored in a compressed form. Raster images can be created by a variety of input devices and techniques, such as digital cameras, scanners, coordinate-measuring machines, seismographic profiling, airborne radar, and more. They can also be synthesized from arbitrary non-image data, such as mathematical functions or three-dimensional geometric models; the latter being a major sub-area of computer graphics. The field of digital image processing is the study of algorithms for their transformation. === Raster file formats === Most users come into contact with raster images through digital cameras, which use any of several image file formats. Some digital cameras give access to almost all the data captured by the camera, using a raw image format. The Universal Photographic Imaging Guidelines (UPDIG) suggests these formats be used when possible since raw files produce the best quality images. These file formats allow the photographer and the processing agent the greatest level of control and accuracy for output. Their use is inhibited by the prevalence of proprietary information (trade secrets) for some camera makers, but there have been initiatives such as OpenRAW to influence manufacturers to release these records publicly. An alternative may be Digital Negative (DNG), a proprietary Adobe product described as "the public, archival format for digital camera raw data". Although this format is not yet universally accepted, support for the product is growing, and increasingly professional archivists and conservationists, working for respectable organizations, variously suggest or recommend DNG for archival purposes. == Vector == Vector images resulted from mathematical geometry (vector). In mathematical terms, a vector consists of both a magnitude, or length, and a direction. Often, both raster and vector elements will be combined in one image; for example, in the case of a billboard with text (vector) and photographs (raster). Example of vector file types are EPS, PDF, and AI. == Image viewing == Image viewer software displayed on images. Web browsers can display standard internet images formats including JPEG, GIF and PNG. Some can show SVG format which is a standard W3C format. In the past, when the Internet was still slow, it was common to provide "preview" images that would load and appear on the website before being replaced by the main image (to give a preliminary impression). Now Internet is fast enough and this preview image is seldom used. Some scientific images can be very large (for instance, the 46 gigapixel size image of the Milky Way, about 194 GB in size). Such images are difficult to download and are usually browsed online through more complex web interfaces. Some viewers offer a slideshow utility to display a sequence of images. == History == Early digital fax machines such as the Bartlane cable picture transmission system preceded digital cameras and computers by decades. The first picture to be scanned, stored, and recreated in digital pixels was displayed on the Standards Eastern Automatic Computer (SEAC) at NIST. The advancement of digital imagery continued in the early 1960s, alongside development of the space program and in medical research. Projects at the Jet Propulsion Laboratory, MIT, Bell Labs and the University of Maryland, among others, used digital images to advance satellite imagery, wirephoto standards conversion, medical imaging, videophone technology, character recognition, and photo enhancement. Rapid advances in digital imaging began with the introduction of MOS integrated circuits in the 1960s and microprocessors in the early 1970s, alongside progress in related computer memory storage, display technologies, and data compression algorithms. The invention of computerized axial tomography (CAT scanning), using x-rays to produce a digital image of a "slice" through a three-dimensional object, was of great importance to medical diagnostics. As well as origination of digital images, digitization of analog images allowed the enhancement and restoration of archaeological artifacts and began to be used in fields as diverse as nuclear medicine, astronomy, law enforcement, defence and industry. Advances in microprocessor technology paved the way for the development and marketing of charge-coupled devices (CCDs) for use in a wide range of image capture devices and gradually displaced the use of analog film and tape in photography and videography towards the end of the 20th century. The computing power necessary to process digital image capture also allowed computer-generated digital images to achieve a level of refinement close to photorealism. === Digital image sensors === The first semiconductor image sensor was the CCD, developed by Willard S. Boyle and George E. Smith at Bell Labs in 1969. While researching MOS technology, they realized that an electric charge was the analogy of the magnetic bubble and that it could be stored on a tiny MOS capacitor. As it was fairly straightforward to fabricate a series of MOS capacitors in a row, they connected a suitable voltage to them so that the charge could be stepped along from one to the next. The CCD is a semiconductor circuit that was later used in the first digital video cameras for television broadcasting. Early CCD sensors suffered from shutter lag. This was largely resolved with the invention of the pinned photodiode (PPD). It was invented by Nobukazu Teranishi, Hiromitsu Shiraki and Yasuo Ishihara at NEC in 1980. It was a photodetector structure with low lag, low noise, high quantum efficiency and low dark current. In 1987, the PPD began to be incorporated into most CCD devices, becoming a fixture in consumer electronic video cameras and then digital still cameras. Since then, the PPD has been used in nearly all CCD sensors and then CMOS sensors. The NMOS active-pixel sensor (APS) was invented by Olympus in Japan during the mid-1980s. This was enabled by advances in MOS semiconductor device fabrication, with MOSFET scaling reaching smaller micron and then sub-micron levels. The NMOS APS was fabricated by Tsutomu Nakamura's team at Olympus in 1985. The CMOS active-pixel sensor (CMOS sensor) was later developed by Eric Fossum's team at the NASA Jet Propulsion Laboratory in 1993. By 2007, sales of CMOS sensors had surpassed CCD sensors. === Digital image compression === An important development in digital image compression technology was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed in 1972. DCT compression is used in JPEG, which was introduced by the Joint Photographic Experts Group in 1992. JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format on the Internet. == Mosaic == In digital imaging, a mosaic is a combination of non-overlapping images, arranged in some tessellation. Gigapixel images are an example of such digital image mosaics. Satellite imagery are often mosaicked to cover Earth regions. Interactive viewing is provided by virtual-reality photography.

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  • Collaboration-oriented architecture

    Collaboration-oriented architecture

    Collaboration Oriented Architecture (COA) is a computer system that is designed to collaborate, or use services, from systems that are outside of the operators control. Collaboration Oriented Architecture will often use Service Oriented Architecture to deliver the technical framework. Collaboration Oriented Architecture is the ability to collaborate between systems that are based on the Jericho Forum principles or "Commandments". Bill Gates and Craig Mundie (Microsoft) clearly articulated the need for people to work outside of their organizations in a secure and collaborative manner in their opening keynote to the RSA Security Conference in February 2007. Successful implementation of a Collaboration Oriented Architecture implies the ability to successfully inter-work securely over the Internet and will typically mean the resolution of the problems that come with de-perimeterisation. == Etymology == The term Collaboration Oriented Architectures was defined and developed in a meeting of the Jericho Forum at a meeting held at HSBC on 6 July 2007. == Definition == The key elements that qualify a security architecture as a Collaboration Oriented Architecture are as follows; Protocol: Systems use appropriately secure protocols to communicate. Authentication: The protocol is authenticated with user and/or system credentials. Federation: User and/or systems credentials are accepted and validated by systems that are not under your (locus of) control. Network Agnostic: The design does not rely on a secure network, thus it will operate securely from an Intranet to raw-Internet Trust: The collaborating system have the capacity to be able to confirm to a specified degree of confidence that the components in a transaction chain have. Risk: The collaborating systems can make a risk assessment on any transaction based on the communicated levels of required trust, based on the required degree of identity, confidentiality, integrity, availability. == Authentication == Working in a collaborative multi-sourced environment implies the need for authentication, authorization and accountability which must interoperate / exchange outside of your locus / area of control. People/systems must be able to manage permissions of resources and rights of users they don't control There must be capability of trusting an organization, which can authenticate individuals or groups, thus eliminating the need to create separate identities In principle, only one instance of person / system / identity may exist, but privacy necessitates the support for multiple instances, or one instance with multiple facets, often referred to as personas Systems must be able to pass on security credentials /assertions Multiple loci (areas) of control must be supported

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  • Fabric Connect

    Fabric Connect

    Fabric Connect, in computer networking usage, is the name used by Extreme Networks to market an extended implementation of the IEEE 802.1aq and IEEE 802.1ah-2008 standards. The Fabric Connect technology was originally developed by the Enterprise Solutions R&D department within Nortel Networks. In 2009, Avaya, Inc acquired Nortel Networks Enterprise Business Solutions; this transaction included the Fabric Connect intellectual property together with all of the Ethernet Switching platforms that supported it. Subsequently, the Fabric Connect technology became part of the Extreme Networks portfolio by virtue of their 2017 purchase of the Avaya Networking business and assets. It was during the Avaya era that this technology was promoted as the lead element of the Virtual Enterprise Network Architecture (VENA). == Technologies == === Fabric Connect === Fabric Connect's provides network-wide, end-to-end, multi-layer virtualization. A network virtualization capability, based on an enhanced implementation of the IEEE 802.1aq Shortest Path Bridging (SPB) standard, Fabric Connect offers the ability to create a simplified network that can dynamically virtualize elements to efficiently provision and utilize resources, thus reducing the strain on the network and personnel. Extreme Networks base the Fabric Connect technology on the SPB standard, including support for RFC 6329, and have integrated IP Routing and IP Multicast support; this unified technology allows for the replacement of multiple conventional protocols such as Spanning Tree, RIP and/or OSPF, ECMP, and PIM. === Fabric Attach === An adjunct to the Fabric Connect technology, Fabric Attach allows network operators to extend network virtualization directly into conventional wiring closets (using existing non-Fabric Ethernet switches) and automate the provisioning of devices to their appropriate virtual network. This is particularly relevant for the mass of unattended network end-point that are now appearing, such as IP Phones, Wireless Access Points, and IP Cameras. Fabric Attach standardized protocols such as 802.1AB LLDP to exchange credentials and obtain provisioning information that allows "Client" Switches to be automatically re-configured on the fly with parameters that let Traffic Flows Map through to Fabric Connect Edge Switches (aka "Backbone Edge Bridge" in SPB definition) functioning as a Fabric Attach "Server" Switch. This method is described by an IETF "Internet Draft", pending further standardization activity. Fabric Attach is typically used to automate Wiring Closet connectivity, but has the potential to be extensible for use in the Data Center, with Virtual Machines being able to dynamically request VLAN/VSN (Virtual Service Network) assignment based upon application requirements. == Hardware products == === Virtual Services Platform 9000 Series === A range of modular chassis-based products, featuring a carrier-grade Linux operation system, and designed for high-performance deployment scenarios that need to scale to multiple terabits of switching capacity and support 10 and 40 gigabit Ethernet connections, and is designed eventually to support 100 gigabit Ethernet. === Virtual Services Platform 8000 Series === A compact form-factor platform delivering high-density 10/40 gigabit Ethernet connectivity, and targeted at mid-market through to mid-size enterprise core switch applications. === Virtual Services Platform 7000 Series === A range of high-end 10 gigabit Ethernet stackable switches that extend fabric-based networking to the data center top-of-rack. They support 40 gigabit Ethernet via the MDA Slot. === Virtual Services Platform 4000 Series === A range of high-end gigabit Ethernet stackable switches that extend Fabric-based networking to branch and metro locations. === Ethernet Routing Switch 5000 Series === A range of high-end gigabit Ethernet stackable switches that provides enterprise-class desktop features, including PoE, and offers 10 Gbit/s uplink connections. Each Switch supports up to 144 Gbit/s of virtual backplane capacity, delivering up to 1.152 Tbit/s for a system of eight, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 4000 Series === A range of gigabit Ethernet stackable switches that provide enterprise-class desktop features, including PoE/PoE+, and offer 1/10 Gbit/s uplink connections. Each switch supports up to 48 Gbit/s of virtual backplane capacity, delivering up to 384 Gbit/s for a system of 8, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 3500 Series === These entry-level gigabit Ethernet stackable switches provide enterprise-class desktop features, including PoE/PoE+, and 1 Gbit/s uplink connections.

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  • Caffe (software)

    Caffe (software)

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

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  • Open Threat Exchange

    Open Threat Exchange

    Open Threat Exchange (OTX) is a crowd-sourced computer-security platform. It has more than 180,000 participants in 140 countries who share more than 19 million potential threats daily. It is free to use. Founded in 2012, OTX was created and is run by AlienVault (now AT&T Cybersecurity), a developer of commercial and open source solutions to manage cyber attacks. The collaborative threat exchange was created partly as a counterweight to criminal hackers successfully working together and sharing information about viruses, malware and other cyber attacks. == Components == OTX is cloud-hosted. Information sharing covers a wide range of security-related issues, including viruses, malware, intrusion detection and firewalls. Its automated tools cleanse, aggregate, validate and publish data shared by participants. The OTX platform validates the data, then strips the information identifying the participating contributor. In 2015, OTX 2.0 added a social network, enabling members to share, discuss and research security threats, including via a real-time threat feed. Users can share the IP addresses or websites from where attacks originated or look up specific threats to see if anyone has already left such information. Users can subscribe to a “Pulse,” an analysis of a specific threat, including data on IoC, impact, and the targeted software. Pulses can be exported as STIX, JSON, OpenloC, MAEC and CSV, and can be used to update local security products automatically. Users can up-vote and comment on specific pulses to assist others in identifying the most important threats. OTX combines social contributions with automated machine-to-machine tools that integrate with major security products such as firewalls and perimeter security hardware. The platform can read security reports in .pdf, .csv, .json and other open formats. Relevant information is extracted automatically, assisting IT professionals in analyzing data more readily. Specific OTX components include a dashboard with details about the top malicious IPs around the world and to check the status of specific IPs; notifications should an organization's IP or domain be found in a hacker forum, blacklist or be listed by OTX; and a feature to review log files to determine if there has been communication with known malicious IPs. In 2016, AlienVault released a new version of OTX, allowing participants to create private communities and discussion groups to share information on threats only within the group. The feature is intended to facilitate more in-depth discussions on specific threats, particular industries, and different regions worldwide. Threat data from groups can also be distributed to subscribers of managed service providers using OTX." == Technology == OTX is a large data platform that integrates natural language processing and machine learning. It uses these features to facilitate the collection and correlation of data from many sources, including third-party threat feeds, websites, external APIs and local agents. == Partners == In 2015, AlienVault partnered with Intel to coordinate real-time threat information on OTX. A similar deal with Hewlett Packard was announced the same year. == Competitors == Both Facebook and IBM have threat exchange platforms. The Facebook ThreatExchange is in beta and requires an application or invitation to join. IBM launched IBM X-Force Exchange in April 2015.

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  • Vatican News App

    Vatican News App

    The Vatican News App is an official mobile application software issued by the Vatican's Dicastery for Communication. Formerly titled The Pope App, the app was launched on January 23, 2013, under the auspices of the Pontifical Council for Social Communications, a now-defunct dicastery that was merged into the Secretariat (now Dicastery) for Communication in March 2016. Initially, The Pope App was available only on iOS devices, but became available for Android phones at the end of February 2013. The app is available for download on iOS and Android in five languages: English, French, Italian, Portuguese and Spanish. It was originally promoted as an application with focus on the figure of the Pope which made it possible to follow the Pope's events while they are taking place. Alerts notified the followers by informing and offering access to "official papal-related content in a variety of formats". The app also enabled its users to see areas of the Vatican through webcams allocated throughout St. Peter's Square in Rome that broadcast images. In early 2018, The Pope App was relaunched as the Vatican News App, accompanied by a redesign that eliminated many of the previous version's features, reducing the app to a more conventional news service, with increased emphasis on news from the Vatican and the worldwide Catholic Church and less focus on the day-to-day activities of the Pope.

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  • NumPy

    NumPy

    NumPy (pronounced NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The predecessor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors. NumPy is fiscally sponsored by NumFOCUS. == History == === matrix-sig === The Python programming language was not originally designed for numerical computing, but attracted the attention of the scientific and engineering community early on. In 1995 the special interest group (SIG) matrix-sig was founded with the aim of defining an array computing package; among its members was Python designer and maintainer Guido van Rossum, who extended Python's syntax (in particular the indexing syntax) to make array computing easier. === Numeric === An implementation of a matrix package was completed by Jim Fulton, then expanded to support multi-dimensional arrays by Jim Hugunin and called Numeric (also variously known as the "Numerical Python extensions" or "NumPy"), with influences from the APL family of languages, Basis, MATLAB, FORTRAN, S and S+, and others. Hugunin, a graduate student at the Massachusetts Institute of Technology (MIT), joined the Corporation for National Research Initiatives (CNRI) in 1997 to work on JPython, leaving Paul Dubois of Lawrence Livermore National Laboratory (LLNL) to take over as maintainer. Other early contributors include David Ascher, Konrad Hinsen and Travis Oliphant. === Numarray === A new package called Numarray was written as a more flexible replacement for Numeric. Like Numeric, it too is now deprecated. Numarray had faster operations for large arrays, but was slower than Numeric on small ones, so for a time both packages were used in parallel for different use cases. The last version of Numeric (v24.2) was released on 11 November 2005, while the last version of numarray (v1.5.2) was released on 24 August 2006. There was a desire to get Numeric into the Python standard library, but Guido van Rossum decided that the code was not maintainable in its state then. === NumPy === In early 2005, NumPy developer Travis Oliphant wanted to unify the community around a single array package and ported Numarray's features to Numeric, releasing the result as NumPy 1.0 in 2006. This new project was part of SciPy. To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0. In 2011, PyPy started development on an implementation of the NumPy API for PyPy. As of 2023, it is not yet fully compatible with NumPy. == Features == NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents due to the absence of compiler optimization. NumPy addresses the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays; using these requires rewriting some code, mostly inner loops, using NumPy. Using NumPy in Python gives functionality comparable to MATLAB since they are both interpreted, and they both allow the user to write fast programs as long as most operations work on arrays or matrices instead of scalars. In comparison, MATLAB boasts a large number of additional toolboxes, notably Simulink, whereas NumPy is intrinsically integrated with Python, a more modern and complete programming language. Moreover, complementary Python packages are available; SciPy is a library that adds more MATLAB-like functionality and Matplotlib is a plotting package that provides MATLAB-like plotting functionality. Although MATLAB can perform sparse matrix operations, NumPy alone cannot perform such operations and requires the use of the scipy.sparse library. Internally, both MATLAB and NumPy rely on BLAS and LAPACK for efficient linear algebra computations. Python bindings of the widely used computer vision library OpenCV utilize NumPy arrays to store and operate on data. Since images with multiple channels are simply represented as three-dimensional arrays, indexing, slicing or masking with other arrays are very efficient ways to access specific pixels of an image. The NumPy array as universal data structure in OpenCV for images, extracted feature points, filter kernels and many more vastly simplifies the programming workflow and debugging. Importantly, many NumPy operations release the global interpreter lock, which allows for multithreaded processing. NumPy also provides a C API, which allows Python code to interoperate with external libraries written in low-level languages. === The ndarray data structure === The core functionality of NumPy is its "ndarray", for n-dimensional array, data structure. These arrays are strided views on memory. In contrast to Python's built-in list data structure, these arrays are homogeneously typed: all elements of a single array must be of the same type. Such arrays can also be views into memory buffers allocated by C/C++, Python, and Fortran extensions to the CPython interpreter without the need to copy data around, giving a degree of compatibility with existing numerical libraries. This functionality is exploited by the SciPy package, which wraps a number of such libraries (notably BLAS and LAPACK). NumPy has built-in support for memory-mapped ndarrays. === Limitations === Inserting or appending entries to an array is not as trivially possible as it is with Python's lists. The np.pad(...) routine to extend arrays actually creates new arrays of the desired shape and padding values, copies the given array into the new one and returns it. NumPy's np.concatenate([a1,a2]) operation does not actually link the two arrays but returns a new one, filled with the entries from both given arrays in sequence. Reshaping the dimensionality of an array with np.reshape(...) is only possible as long as the number of elements in the array does not change. These circumstances originate from the fact that NumPy's arrays must be views on contiguous memory buffers. Algorithms that are not expressible as a vectorized operation will typically run slowly because they must be implemented in "pure Python", while vectorization may increase memory complexity of some operations from constant to linear, because temporary arrays must be created that are as large as the inputs. Runtime compilation of numerical code has been implemented by several groups to avoid these problems; open source solutions that interoperate with NumPy include numexpr and Numba. Cython and Pythran are static-compiling alternatives to these. Many modern large-scale scientific computing applications have requirements that exceed the capabilities of the NumPy arrays. For example, NumPy arrays are usually loaded into a computer's memory, which might have insufficient capacity for the analysis of large datasets. Further, NumPy operations are executed on a single CPU. However, many linear algebra operations can be accelerated by executing them on clusters of CPUs or of specialized hardware, such as GPUs and TPUs, which many deep learning applications rely on. As a result, several alternative array implementations have arisen in the scientific python ecosystem over the recent years, such as Dask for distributed arrays and TensorFlow or JAX for computations on GPUs. Because of its popularity, these often implement a subset of NumPy's API or mimic it, so that users can change their array implementation with minimal changes to their code required. A library named CuPy, accelerated by Nvidia's CUDA framework, has also shown potential for faster computing, being a 'drop-in replacement' of NumPy. == Examples == NumPy is conventionally imported as np. === Basic operations === === Universal functions === === Linear algebra === === Multidimensional arrays === === Incorporation with OpenCV === === Nearest-neighbor search === Functional Python and vectorized NumPy version. === F2PY === Quickly wrap native code for faster scripts.

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  • Open Threat Exchange

    Open Threat Exchange

    Open Threat Exchange (OTX) is a crowd-sourced computer-security platform. It has more than 180,000 participants in 140 countries who share more than 19 million potential threats daily. It is free to use. Founded in 2012, OTX was created and is run by AlienVault (now AT&T Cybersecurity), a developer of commercial and open source solutions to manage cyber attacks. The collaborative threat exchange was created partly as a counterweight to criminal hackers successfully working together and sharing information about viruses, malware and other cyber attacks. == Components == OTX is cloud-hosted. Information sharing covers a wide range of security-related issues, including viruses, malware, intrusion detection and firewalls. Its automated tools cleanse, aggregate, validate and publish data shared by participants. The OTX platform validates the data, then strips the information identifying the participating contributor. In 2015, OTX 2.0 added a social network, enabling members to share, discuss and research security threats, including via a real-time threat feed. Users can share the IP addresses or websites from where attacks originated or look up specific threats to see if anyone has already left such information. Users can subscribe to a “Pulse,” an analysis of a specific threat, including data on IoC, impact, and the targeted software. Pulses can be exported as STIX, JSON, OpenloC, MAEC and CSV, and can be used to update local security products automatically. Users can up-vote and comment on specific pulses to assist others in identifying the most important threats. OTX combines social contributions with automated machine-to-machine tools that integrate with major security products such as firewalls and perimeter security hardware. The platform can read security reports in .pdf, .csv, .json and other open formats. Relevant information is extracted automatically, assisting IT professionals in analyzing data more readily. Specific OTX components include a dashboard with details about the top malicious IPs around the world and to check the status of specific IPs; notifications should an organization's IP or domain be found in a hacker forum, blacklist or be listed by OTX; and a feature to review log files to determine if there has been communication with known malicious IPs. In 2016, AlienVault released a new version of OTX, allowing participants to create private communities and discussion groups to share information on threats only within the group. The feature is intended to facilitate more in-depth discussions on specific threats, particular industries, and different regions worldwide. Threat data from groups can also be distributed to subscribers of managed service providers using OTX." == Technology == OTX is a large data platform that integrates natural language processing and machine learning. It uses these features to facilitate the collection and correlation of data from many sources, including third-party threat feeds, websites, external APIs and local agents. == Partners == In 2015, AlienVault partnered with Intel to coordinate real-time threat information on OTX. A similar deal with Hewlett Packard was announced the same year. == Competitors == Both Facebook and IBM have threat exchange platforms. The Facebook ThreatExchange is in beta and requires an application or invitation to join. IBM launched IBM X-Force Exchange in April 2015.

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  • Wave Financial

    Wave Financial

    Wave is a Canadian company that provides financial services and software for small businesses. Wave is headquartered in the East Bayfront neighbourhood in Toronto, Canada. The company's first product was free online accounting software designed for businesses with 1–9 employees, followed by invoicing, personal finance and receipt-scanning software (OCR). In 2012, Wave began branching into financial services, initially with Payments by Wave (credit card processing) and Payroll by Wave, followed in February 2017 by Lending by Wave, which has since been discontinued. == History == CEO Kirk Simpson and CPO James Lochrie launched Wave Accounting Inc. in July 2009, Wave Accounting launched to the public on November 16, 2010. In June 2011, Series A funding led by OMERS Ventures was closed. In September 2011, FedDev Ontario invested one million dollars in funding. In October 2011, a $5-million investment led by U.S. venture capital firm Charles River Ventures was announced. In May 2012, Wave Accounting closed its series B financing round led by The Social+Capital Partnership, with follow-on participation from Charles River Ventures and OMERS Ventures. Wave acquired a company called Small Payroll in November 2011, which was later launched as a payroll product called Wave Payroll. In February 2012, Wave officially launched Wave Payroll to the public in Canada, followed by the American release in November of the same year. In August, 2012, the company announced the acquisition of Vuru.co, an online stock-tracking service. Terms of the deal were not disclosed. In December 2012, the company rebranded itself as Wave to emphasize its broadened spectrum of services. On March 14, 2019, the company acquired Every, a Toronto-based fintech company that provides business accounts and debit cards to small businesses. On June 11, 2019, the company announced it was being acquired by tax preparation company, H&R Block, for $537 million. On June 15, 2022, Wave announced that Kirk Simpson would be leaving and being replaced as CEO by Zahir Khoja. In May 2025, US customers of Wave were transitioned to a new Payroll processing system supported by CheckHQ. The new integration improved support for US employers by handling employer tax withholding and payments in all 50 US States. == Products == The company's initial product, Accounting by Wave, is a double entry accounting tool. Services include direct bank data imports, invoicing and expense tracking, customizable chart of accounts, and journal transactions. Accounting by Wave integrates with expense tracking software Shoeboxed and e-commerce website Etsy. The next product launched was Payroll by Wave, which was launched in 2012 after the acquisition of SmallPayroll.ca. Payroll by Wave is only available in the US and Canada. Invoicing by Wave is an offshoot of the company's earlier accounting tools. Additional products launched on or shortly after the company's rebrand in December 2012 include: a credit card processing tool, Payments by Wave, built initially on integration with Stripe credit card processing. However, Wave does not report merchant fees correctly for countries where Stripe charges a tax such as GST. In these cases, the merchant fees are reported without tax and do not match your Stripe account. a receipt scanning tool, Receipts by Wave. In 2017, Wave signed an agreement to provide its platform on RBC's online business banking site. The RBC-Wave service will be co-branded. == Taxes supported == The company's software supports tax-exclusive pricing, such as U.S. sales tax, where taxes are added on top of prices quoted. This has two effects: When scanning receipts users must manually add the tax, and input the amount. When making an invoice, users must put in a price before tax, and the system will add the tax on top. This makes Wave unable to handle taxes in countries like Australia where prices must be quoted inclusive of all taxes, such as GST. There is no way to set an invoice total and have Wave calculate the tax portion as a percentage. == Pricing and business model == As of June 10, 2024, Wave offers two tiers for its software: a free Starter plan with limitations on some features, and a paid Pro plan. In addition to its paid plan, revenue from the company comes from other paid financial services the company offers: Payments by Wave: Card processing which includes debit, credit and prepaid cards as well as ACH (bank payments) in the United States. Fees are a percentage of the transaction. Payroll by Wave: Monthly subscription fee plus usage fees. Wave previously included advertising on its pages as a source of revenue. Advertising was removed in January 2017. In 2017, Wave raised $24m (USD) in funding led by NAB Ventures. In 2019, H&R Block announced the acquisition of Wave in a cash deal worth $405 million USD.

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  • Google Clips

    Google Clips

    Google Clips is a discontinued miniature clip-on camera device developed by Google. == History == It was announced on October 4, 2017 and went on sale on January 27, 2018. Google Clips automatically captured video clips (without audio) at moments its machine learning algorithms determined to be interesting or relevant. An indicator flashed when the camera was looking for scenes to capture. Google Clips' artificial intelligence (AI) could learn the faces of people to take photographs with certain people, and could automatically set lighting and framing. It had 16 GB of storage built-in storage and could record clips for up to 3 hours. This camera was originally priced at US$249 in the United States. It was withdrawn from sale on October 15, 2019, but supported until the end of December 2021. == Reception == The Independent wrote that Google Clips is "an impressive little device, but one that also has the potential to feel very creepy." According to The Verge's generally negative review, "it didn't capture anything special" over two weeks of testing.

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  • Box blur

    Box blur

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

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  • Research software engineering

    Research software engineering

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

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