AI Chatbot Quill

AI Chatbot Quill — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  MobileNet

  MobileNet is a family of convolutional neural network (CNN) architectures designed for image classification, object detection, and other computer vision tasks. They are designed for small size, low latency, and low power consumption, making them suitable for on-device inference and edge computing on resource-constrained devices like mobile phones and embedded systems. They were originally designed to be run efficiently on mobile devices with TensorFlow Lite. The need for efficient deep learning models on mobile devices led researchers at Google to develop MobileNet. As of June 2025, the family has five versions, each improving upon the previous one in terms of performance and efficiency. == Features == === V1 === MobileNetV1 was published in April 2017. Its main architectural innovation was incorporation of depthwise separable convolutions. It was first developed by Laurent Sifre during an internship at Google Brain in 2013 as an architectural variation on AlexNet to improve convergence speed and model size. The depthwise separable convolution decomposes a single standard convolution into two convolutions: a depthwise convolution that filters each input channel independently and a pointwise convolution ( 1 × 1 {\displaystyle 1\times 1} convolution) that combines the outputs of the depthwise convolution. This factorization significantly reduces computational cost. The MobileNetV1 has two hyperparameters: a width multiplier α {\displaystyle \alpha } that controls the number of channels in each layer. Smaller values of α {\displaystyle \alpha } lead to smaller and faster models, but at the cost of reduced accuracy, and a resolution multiplier ρ {\displaystyle \rho } , which controls the input resolution of the images. Lower resolutions result in faster processing but potentially lower accuracy. === V2 === MobileNetV2 was published in March 2019. It uses inverted residual layers and linear bottlenecks. Inverted residuals modify the traditional residual block structure. Instead of compressing the input channels before the depthwise convolution, they expand them. This expansion is followed by a 1 × 1 {\displaystyle 1\times 1} depthwise convolution and then a 1 × 1 {\displaystyle 1\times 1} projection layer that reduces the number of channels back down. This inverted structure helps to maintain representational capacity by allowing the depthwise convolution to operate on a higher-dimensional feature space, thus preserving more information flow during the convolutional process. Linear bottlenecks removes the typical ReLU activation function in the projection layers. This was rationalized by arguing that that nonlinear activation loses information in lower-dimensional spaces, which is problematic when the number of channels is already small. === V3 === MobileNetV3 was published in 2019. The publication included MobileNetV3-Small, MobileNetV3-Large, and MobileNetEdgeTPU (optimized for Pixel 4). They were found by a form of neural architecture search (NAS) that takes mobile latency into account, to achieve good trade-off between accuracy and latency. It used piecewise-linear approximations of swish and sigmoid activation functions (which they called "h-swish" and "h-sigmoid"), squeeze-and-excitation modules, and the inverted bottlenecks of MobileNetV2. === V4 === MobileNetV4 was published in September 2024. The publication included a large number of architectures found by NAS. Inspired by Vision Transformers, the V4 series included multi-query attention. It also unified both inverted residual and inverted bottleneck from the V3 series with the "universal inverted bottleneck", which includes these two as special cases. === V5 === MobileNetV5's architecture was published shortly after the release of Gemma 3n in June 2025. While the announcement stated a technical report on MobileNetV5 would be available soon, this has not yet materialised. The network is 10 times larger than the largest V4 variant.

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  Space partitioning

  In geometry, space partitioning is the process of dividing an entire space (usually a Euclidean space) into two or more disjoint subsets (see also partition of a set). In other words, space partitioning divides a space into non-overlapping regions. Any point in the space can then be identified to lie in exactly one of the regions. == Overview == Space-partitioning systems are often hierarchical, meaning that a space (or a region of space) is divided into several regions, and then the same space-partitioning system is recursively applied to each of the regions thus created. The regions can be organized into a tree, called a space-partitioning tree. Most space-partitioning systems use planes (or, in higher dimensions, hyperplanes) to divide space: points on one side of the plane form one region, and points on the other side form another. Points exactly on the plane are usually arbitrarily assigned to one or the other side. Recursively partitioning space using planes in this way produces a BSP tree, one of the most common forms of space partitioning. == Uses == === In computer graphics === Space partitioning is particularly important in computer graphics, especially heavily used in ray tracing, where it is frequently used to organize the objects in a virtual scene. A typical scene may contain millions of polygons. Performing a ray/polygon intersection test with each would be a very computationally expensive task. Storing objects in a space-partitioning data structure (k-d tree or BSP tree for example) makes it easy and fast to perform certain kinds of geometry queries—for example in determining whether a ray intersects an object, space partitioning can reduce the number of intersection test to just a few per primary ray, yielding a logarithmic time complexity with respect to the number of polygons. Space partitioning is also often used in scanline algorithms to eliminate the polygons out of the camera's viewing frustum, limiting the number of polygons processed by the pipeline. There is also a usage in collision detection: determining whether two objects are close to each other can be much faster using space partitioning. === In integrated circuit design === In integrated circuit design, an important step is design rule check. This step ensures that the completed design is manufacturable. The check involves rules that specify widths and spacings and other geometry patterns. A modern design can have billions of polygons that represent wires and transistors. Efficient checking relies heavily on geometry query. For example, a rule may specify that any polygon must be at least n nanometers from any other polygon. This is converted into a geometry query by enlarging a polygon by n/2 at all sides and query to find all intersecting polygons. === In probability and statistical learning theory === The number of components in a space partition plays a central role in some results in probability theory. See Growth function for more details. === In geography and GIS === There are many studies and applications where Geographical Spatial Reality is partitioned by hydrological criteria, administrative criteria, mathematical criteria or many others. In the context of cartography and GIS - Geographic Information System, is common to identify cells of the partition by standard codes. For example the for HUC code identifying hydrographical basins and sub-basins, ISO 3166-2 codes identifying countries and its subdivisions, or arbitrary DGGs - discrete global grids identifying quadrants or locations. == Data structures == Common space-partitioning systems include: BSP trees Quadtrees Octrees k-d trees Bins == Number of components == Suppose the n-dimensional Euclidean space is partitioned by r {\displaystyle r} hyperplanes that are ( n − 1 ) {\displaystyle (n-1)} -dimensional. What is the number of components in the partition? The largest number of components is attained when the hyperplanes are in general position, i.e, no two are parallel and no three have the same intersection. Denote this maximum number of components by C o m p ( n , r ) {\displaystyle Comp(n,r)} . Then, the following recurrence relation holds: C o m p ( n , r ) = C o m p ( n , r − 1 ) + C o m p ( n − 1 , r − 1 ) {\displaystyle Comp(n,r)=Comp(n,r-1)+Comp(n-1,r-1)} C o m p ( 0 , r ) = 1 {\displaystyle Comp(0,r)=1} - when there are no dimensions, there is a single point. C o m p ( n , 0 ) = 1 {\displaystyle Comp(n,0)=1} - when there are no hyperplanes, all the space is a single component. And its solution is: C o m p ( n , r ) = ∑ k = 0 n ( r k ) {\displaystyle Comp(n,r)=\sum _{k=0}^{n}{r \choose k}} if r ≥ n {\displaystyle r\geq n} C o m p ( n , r ) = 2 r {\displaystyle Comp(n,r)=2^{r}} if r ≤ n {\displaystyle r\leq n} (consider e.g. r {\displaystyle r} perpendicular hyperplanes; each additional hyperplane divides each existing component to 2). which is upper-bounded as: C o m p ( n , r ) ≤ r n + 1 {\displaystyle Comp(n,r)\leq r^{n}+1}

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  Spanner (database)

  Spanner is a distributed SQL database management and storage service developed by Google. It provides features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover. Spanner is used in Google F1, the database for its advertising business Google Ads, as well as Gmail and Google Photos. == Features == Spanner stores large amounts of mutable structured data. Spanner allows users to perform arbitrary queries using SQL with relational data while maintaining strong consistency and high availability for that data with synchronous replication. Key features of Spanner: Transactions can be applied across rows, columns, tables, and databases within a Spanner universe. Clients can control the replication and placement of data using automatic multi-site replication and failover. Replication is synchronous and strongly consistent. Reads are strongly consistent and data is versioned to allow for stale reads: clients can read previous versions of data, subject to garbage collection windows. Supports a native SQL interface for reading and writing data. Support for Graph Query Language == History == Spanner was first described in 2012 for internal Google data centers. Spanner's SQL capability was added in 2017 and documented in a SIGMOD 2017 paper. It became available as part of Google Cloud Platform in 2017, under the name "Cloud Spanner". == Architecture == Spanner uses the Paxos algorithm as part of its operation to shard (partition) data across up to hundreds of servers. It makes heavy use of hardware-assisted clock synchronization using GPS clocks and atomic clocks to ensure global consistency. TrueTime is the brand name for Google's distributed cloud infrastructure, which provides Spanner with the ability to generate monotonically increasing timestamps in data centers around the world. Google's F1 SQL database management system (DBMS) is built on top of Spanner, replacing Google's custom MySQL variant.

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  Intel Threat Detection Technology

  Intel Threat Detection Technology (TDT) is a CPU-level technology created by Intel in 2018 to enable host endpoint protections to use a CPU's low-level access to detect threats to a system. TDT consists of multiple components including Accelerated Memory Scanning, which uses the CPU's integrated GPU to scan memory, and Advanced Platform Telemetry, which uses processor-level activity monitoring to detect unusual activity. It is supported on sixth-generation or newer Intel Core CPUs and additional capabilities were added to the 11th generation Core processors. Intel TDT is integrated into several third-party anti-malware solutions including Microsoft Defender, Check Point Harmony Endpoint, CrowdStrike Falcon, and others. == Accelerated Memory Scanning == Accelerated Memory Scanning (also referred to as "Advanced Memory Scanning") uses the CPU's integrated GPU to scan memory for malicious code, instead of using the CPU directly. This improves system responsiveness during anti-malware scanning. and lowers power consumption. Features include pattern matching, using random forest decision trees, string extraction, entropy calculation, and Euclidean clustering. == Advanced Platform Telemetry == Advanced Platform Telemetry collects CPU-level telemetry to detect uncommon activity patterns which might be indicative of malware. The telemetry data is collected from the CPU performance monitoring unit (PMU) and doesn't require a large signature database to detect malware. Instead, it uses machine-learning based correlations to identify indicators of attack For example, Microsoft Defender is able to use TDT's Advanced Platform Telemetry features to detect processor usage patterns indicative of ransomware and cryptojacking with TDT so it can detect them.

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  Software design

  Software design is the process of conceptualizing how a software system will work before it is implemented or modified. Software design also refers to the direct result of the design process – the concepts of how the software will work which may be formally documented or may be maintained less formally, including via oral tradition. The design process enables a designer to model aspects of a software system before it exists with the intent of making the effort of writing the code more efficiently. Creativity, past experience, a sense of what makes "good" software, and a commitment to quality are success factors for a competent design. A software design can be compared to an architected plan for a house. High-level plans represent the totality of the house (e.g., a three-dimensional rendering of the house). Lower-level plans provide guidance for constructing each detail (e.g., the plumbing lay). Similarly, the software design model provides a variety of views of the proposed software solution. == Part of the overall process == In terms of the waterfall development process, software design is the activity that occurs after requirements analysis and before coding. Requirements analysis determines what the system needs to do without determining how it will do it, and thus, multiple designs can be imagined that satisfy the requirements. The design can be created while coding, without a plan or requirements analysis, but for more complex projects this is less feasible. Completing a design prior to coding allows for multidisciplinary designers and subject-matter experts to collaborate with programmers to produce software that is useful and technically sound. Sometimes, a simulation or prototype is created to model the system in an effort to determine a valid and good design. == Code as design == A common point of confusion with the term design in software is that the process applies at multiple levels of abstraction such as a high-level software architecture and lower-level components, functions and algorithms. A relatively formal process may occur at high levels of abstraction but at lower levels, the design process is almost always less formal where the only artifact of design may be the code itself. To the extent that this is true, software design refers to the design of the design. Edsger W. Dijkstra referred to this layering of semantic levels as the "radical novelty" of computer programming, and Donald Knuth used his experience writing TeX to describe the futility of attempting to design a program prior to implementing it: TEX would have been a complete failure if I had merely specified it and not participated fully in its initial implementation. The process of implementation constantly led me to unanticipated questions and to new insights about how the original specifications could be improved. == Artifacts == A design process may include the production of art Software design documentation such as flow chart, use case, Pseudocode, Unified Modeling Language model and other Fundamental modeling concepts. For user centered software, design may involve user experience design yielding a storyboard to help determine those specifications. Documentation may be reviewed to allow constraints, specifications and even requirements to be adjusted prior to coding. == Iterative design == Software systems inherently deal with uncertainties, and the size of software components can significantly influence a system's outcomes, both positively and negatively. Neal Ford and Mark Richards propose an iterative approach to address the challenge of identifying and right-sizing components. This method emphasizes continuous refinement as teams develop a more nuanced understanding of system behavior and requirements. The approach typically involves a cycle with several stages: A high-level partitioning strategy is established, often categorized as technical or domain-based. Guidelines for the smallest meaningful deployable unit, referred to as "quanta," are defined. While these foundational decisions are made early, they may be revisited later in the cycle if necessary. Initial components are identified based on the established strategy. Requirements are assigned to the identified components. The roles and responsibilities of each component are analyzed to ensure clarity and minimize overlap. Architectural characteristics, such as scalability, fault tolerance, and maintainability, are evaluated. Components may be restructured based on feedback from development teams. This cycle serves as a general framework and can be adapted to different domains. == Design principles == Design principles enable a software engineer to navigate the design process. Davis suggested principles which have been refined over time as: The design process should not suffer from "tunnel vision" A good designer should consider alternative approaches, judging each based on the requirements of the problem, the resources available to do the job. The design should be traceable to the analysis model Because a single element of the design model can often be traced back to multiple requirements, it is necessary to have a means for tracking how requirements have been satisfied by the design model. The design should not reinvent the wheel Systems are constructed using a set of design patterns, many of which have likely been encountered before. These patterns should always be chosen as an alternative to reinvention. Time is short and resources are limited; design time should be invested in representing (truly new) ideas by integrating patterns that already exist (when applicable). The design should "minimize the intellectual distance" between the software and the problem as it exists in the real world That is, the structure of the software design should, whenever possible, mimic the structure of the problem domain. The design should exhibit uniformity and integration A design is uniform if it appears fully coherent. In order to achieve this outcome, rules of style and format should be defined for a design team before design work begins. A design is integrated if care is taken in defining interfaces between design components. The design should be structured to accommodate change The design concepts discussed in the next section enable a design to achieve this principle. The design should be structured to degrade gently, even when aberrant data, events, or operating conditions are encountered Well-designed software should never "bomb"; it should be designed to accommodate unusual circumstances, and if it must terminate processing, it should do so in a graceful manner. Design is not coding, coding is not design Even when detailed procedural designs are created for program components, the level of abstraction of the design model is higher than the source code. The only design decisions made at the coding level should address the small implementation details that enable the procedural design to be coded. The design should be assessed for quality as it is being created, not after the fact A variety of design concepts and design measures are available to assist the designer in assessing quality throughout the development process. The design should be reviewed to minimize conceptual (semantic) errors There is sometimes a tendency to focus on minutiae when the design is reviewed, missing the forest for the trees. A design team should ensure that major conceptual elements of the design (omissions, ambiguity, inconsistency) have been addressed before worrying about the syntax of the design model. == Design concepts == Design concepts provide a designer with a foundation from which more sophisticated methods can be applied. Design concepts include: Abstraction Reducing the information content of a concept or an observable phenomenon, typically to retain only information that is relevant for a particular purpose. It is an act of Representing essential features without including the background details or explanations. Architecture The overall structure of the software and the ways in which that structure provides conceptual integrity for a system. Good software architecture will yield a good return on investment with respect to the desired outcome of the project, e.g. in terms of performance, quality, schedule and cost. Control hierarchy A program structure that represents the organization of a program component and implies a hierarchy of control. Data structure Representing the logical relationship between elements of data. Design pattern A designer may identify a design aspect of the system that has solved in the past. The reuse of such patterns can increase software development velocity. Information hiding Modules should be specified and designed so that information contained within a module is inaccessible to other modules that have no need for such information. Modularity Dividing the solution into parts (modules). Refinement The process of elaboration. A hierarchy is developed by decomposing a macrosco

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  Image tracing

  In computer graphics, image tracing, raster-to-vector conversion or raster vectorization is the conversion of raster graphics into vector graphics. == Background == An image does not have any structure: it is just a collection of marks on paper, grains in film, or pixels in a bitmap. While such an image is useful, it has some limits. If the image is magnified enough, its artifacts appear. The halftone dots, film grains, and pixels become apparent. Images of sharp edges become fuzzy or jagged. See, for example, pixelation. Ideally, a vector image does not have the same problem. Edges and filled areas are represented as mathematical curves or gradients, and they can be magnified arbitrarily (though of course the final image must also be rasterized in to be rendered, and its quality depends on the quality of the rasterization algorithm for the given inputs). The task in vectorization is to convert a two-dimensional image into a two-dimensional vector representation of the image. It is not examining the image and attempting to recognize or extract a three-dimensional model that may be depicted; i.e. it is not a vision system. For most applications, vectorization also does not involve optical character recognition; characters are treated as lines, curves, or filled objects without attaching any significance to them. In vectorization, the shape of the character is preserved, so artistic embellishments remain. Vectorization is the inverse operation corresponding to rasterization, as integration is to differentiation. And, just as with these other operations, while rasterization is fairly straightforward and algorithmic, vectorization involves the reconstruction of lost information and therefore requires heuristic methods. Synthetic images such as maps, cartoons, logos, clip art, and technical drawings are suitable for vectorization. Those images could have been originally made as vector images because they are based on geometric shapes or drawn with simple curves. Continuous tone photographs (such as live portraits) are not good candidates for vectorization. The input to vectorization is an image, but an image may come in many forms such as a photograph, a drawing on paper, or one of several raster file formats. Programs that do raster-to-vector conversion may accept bitmap formats such as TIFF, BMP and PNG. The output is a vector file format. Common vector formats are SVG, DXF, EPS, EMF and AI. Vectorization can be used to update images or recover work. Personal computers often come with a simple paint program that produces a bitmap output file. These programs allow users to make simple illustrations by adding text, drawing outlines, and filling outlines with a specific color. Only the results of these operations (the pixels) are saved in the resulting bitmap; the drawing and filling operations are discarded. Vectorization can be used to recapture some of the information that was lost. Vectorization is also used to recover information that was originally in a vector format but has been lost or has become unavailable. A company may have commissioned a logo from a graphic arts firm. Although the graphics firm used a vector format, the client company may not have received a copy of that format. The company may then acquire a vector format by scanning and vectorizing a paper copy of the logo. == Process == Vectorization starts with an image. === Manual === The image can be vectorized manually. A person could look at the image, make some measurements, and then write the output file by hand. That was the case for the vectorization of a technical illustration about neutrinos. The illustration has a few geometric shapes and a lot of text; it was relatively easy to convert the shapes, and the SVG vector format allows the text (even subscripts and superscripts) to be entered easily. The original image did not have any curves (except for the text), so the conversion is straightforward. Curves make the conversion more complicated. Manual vectorization of complicated shapes can be facilitated by the tracing function built into some vector graphics editing programs. If the image is not yet in machine readable form, then it has to be scanned into a usable file format. Once there is a machine-readable bitmap, the image can be imported into a graphics editing program (such as Adobe Illustrator, CorelDRAW, or Inkscape). Then a person can manually trace the elements of the image using the program's editing features. Curves in the original image can be approximated with lines, arcs, and Bézier curves. An illustration program allows spline knots to be adjusted for a close fit. Manual vectorization is possible, but it can be tedious. Although graphics drawing programs have been around for a long time, artists may find the freehand drawing facilities awkward even when a drawing tablet is used. Instead of using a program, Pepper recommends making an initial sketch on paper. Instead of scanning the sketch and tracing it freehand in the computer, Pepper states: "Those proficient with a graphic tablet and stylus could make the following changes directly in CorelDRAW by using a scan of the sketch as an underlay and drawing over it. I prefer to use pen and ink, and a light table"; most of the final image was traced by hand in ink. Later the line-drawing image was scanned at 600 dpi, cleaned up in a paint program, and then automatically traced with a program. Once the black and white image was in the graphics program, some other elements were added and the figure was colored. Similarly, Ploch recreated a design from a digital photograph. The JPEG was imported and some "basic shapes" were traced by hand and colored in the graphics drawing program; more complex shapes were handled differently. Ploch used a bitmap editor to remove the background and crop the more complex image components. He then printed the image and traced it by hand onto tracing paper to get a clean black and white line drawing. That drawing was scanned and then vectorized with a program. === Automatic === Some programs automate the vectorization process. Example programs are Adobe Illustrator, Inkscape, Corel's PowerTRACE, and Potrace. Some of these programs have a command line interface while others are interactive that allow the user to adjust the conversion settings and view the result. Adobe Streamline is not only an interactive program, but it also allows a user to manually edit the input bitmap and the output curves. Corel's PowerTRACE is accessed through CorelDRAW; CorelDRAW can be used to modify the input bitmap and edit the output curves. Adobe Illustrator has a facility to trace individual curves. Automated programs can have mixed results. A program (PowerTRACE) was used to convert a PNG map to SVG. The program did a good job on the map boundaries (the most tedious task in the tracing) and the settings dropped out all the text (small objects). The text was manually re-inserted. Other conversions may not go as well. The results depend on having high-quality scans, reasonable settings, and good algorithms. Scanned images often have a lot of noise, which can require additional work to clean up. == Options == There are many different image styles and possibilities, and no single vectorization method works well on all images. Consequently, vectorization programs have many options that influence the result. One issue is what the predominant shapes are. If the image is of a fill-in form, then it will probably have just vertical and horizontal lines of a constant width. The program's vectorization should take that into account. On the other hand, a CAD drawing may have lines at any angle, there may be curved lines, and there may be several line weights (thick for objects and thin for dimension lines). Instead of (or in addition to) curves, the image may contain outlines filled with the same color. Adobe Streamline allows users to select a combination of line recognition (horizontal and vertical lines), centerline recognition, or outline recognition. Streamline also allows small outline shapes to be thrown out; the notion is such small shapes are noise. The user may set the noise level between 0 and 1000; an outline that has fewer pixels than that setting is discarded. Another issue is the number of colors in the image. Even images that were created as black on white drawings may end up with many shades of gray. Some line-drawing routines employ anti-aliasing; a pixel completely covered by the line will be black, but a pixel that is only partially covered will be gray. If the original image is on paper and is scanned, there is a similar result: edge pixels will be gray. Sometimes images are compressed (e.g., JPEG images), and the compression will introduce gray levels. Many of the vectorization programs will group same-color pixels into lines, curves, or outlined shapes. If each possible color is grouped into its object, there can be an enormous number of objects. Instead, the user is asked to s

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  Stanza Living

  Stanza Living is the common brand name for Dtwelve Spaces Private Limited. It provides fully-managed shared living accommodations to students and young professionals. Founded by Anindya Dutta and Sandeep Dalmia, the company is present across 23 cities including Delhi, NCR, Bangalore, Visakhapatnam, Hyderabad, Chennai, Coimbatore, Indore, Pune, Baroda, Vijayawada, and Dehradun, Kota in India, with a capacity of 70,000 beds. Stanza Living is a technology-enabled housing concept which provides fully-furnished residences with amenities like meals, internet, laundry services, housekeeping, security and community engagement programmes. The company has an asset-light business model under which it engages in long-term lease agreements with property owners/developers, who convert their assets into shared living residences as per company guidelines. These assets are subsequently operated by Stanza Living. == Industry background == A report by Cushman & Wakefield (C&W) titled 'Exploring the Student Housing Universe in India City Insights', estimates that there were over 9.08 million migrant student enrolments in India's higher educational institutions (HEIs) for the year 2018-19 who need quality accommodation facilities. According to the report, Delhi-NCR, Mumbai, and Pune are the three biggest markets for student housing in the country, and these cities require an additional 4.75 lakh beds from organized co-living operators to meet the current demand. == History == Stanza Living provides tech-enabled, fully managed community living facilities for students and working professionals. The company was launched as a student housing business in Delhi NCR with a capacity of 100 beds, and grew to 14 cities by 2019. By early 2020, the company began catering to working professionals as well. The company has a combined inventory of 70,000 beds under management for both students and working professionals. Stanza Living is currently valued at $300 million. It has raised a capital of about $70 million from leading global investors like Falcon Edge Capital, Sequoia Capital, Matrix Partners and Accel Partners. November 2017 – Seed funding, September 2018 – Series A, March 2019 – Debt financing, July 2019 – Series C round, December 2019 - Debt financing. The company has invested in building technology products for business efficiency and consumer experience, like the Stanza Resident App and Stanza Real Estate App. Stanza Living has close to 1,500 employees across India. It is recognized among Top Real Estate Tech Startups of 2020 across the globe by research and analysis company Tracxn. The company has been shortlisted among Top 25 Start-ups of India in 2019 by LinkedIn == Founders == Stanza Living was co-founded by Anindya Dutta and Sandeep Dalmia. Sandeep Dalmia is an alumnus of Delhi College of Engineering and IIM Ahmedabad. Prior to Stanza, he was a Principal at Boston Consulting Group, working across India, US and South East Asia markets. Anindya Dutta was previously a Real Estate investor with Oaktree Capital and prior to that, he worked at Goldman Sachs in London. He is an alumnus of IIT Kharagpur and IIM Ahmedabad.

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  Kdb+

  kdb+ is a column-based relational time series database (TSDB) with in-memory (IMDB) abilities, developed and marketed by KX Systems. The database is commonly used in high-frequency trading (HFT) to store, analyze, process, and retrieve large data sets at high speed. kdb+ has the ability to handle billions of records and analyzes data within a database. The database is available in 32-bit and 64-bit versions for several operating systems. Financial institutions use kdb+ to analyze time series data such as stock or commodity exchange data. The database has also been used for other time-sensitive data applications including commodity markets such as energy trading, telecommunications, sensor data, log data, machine and computer network usage monitoring along with real time analytics in Formula One racing. == Overview == kdb+ is a high-performance column-store database that was designed to process and store large amounts of data. Commonly accessed data is pushed into random-access memory (RAM), which is faster to access than data in disk storage. Created with financial institutions in mind, the database was developed as a central repository to store time series data that supports real-time analysis of billions of records. kdb+ has the ability to analyze data over time and responds to queries similar to Structured Query Language (SQL). Columnar databases return answers to some queries in a more efficient way than row-based database management systems. kdb+ dictionaries, tables and nanosecond time stamps are native data types and are used to store time series data. At the core of kdb+ is the built-in programming language, q, a concise, expressive query array language, and dialect of the language APL. Q can manipulate streaming, real-time, and historical data. kdb+ uses q to aggregate and analyze data, perform statistical functions, and join data sets and supports SQL queries The vector language q was built for speed and expressiveness and eliminates most need for looping structures. kdb+ includes interfaces in C, C++, Java, C#, and Python. == History == In 1998, KX released kdb, a database built on the language K written by Arthur Whitney. In 2003, kdb+ was released as a 64-bit version of kdb. In 2004, the kdb+ tick market database framework was released along with kdb+ taq, a loader for the New York Stock Exchange (NYSE) taq data. kdb+ was created by Arthur Whitney, building on his prior work with array languages. In April 2007, KX announced that it was releasing a version of kdb+ for Mac OS X. Then, kdb+ was also available on the operating systems Linux, Windows, and Solaris. In September 2012, version 3.0 was released. It was optimized for Intel's upgraded processors with support for WebSockets, and universally unique identifiers (UUIDs, termed globally unique identifiers (GUID)s in Microsoft software). Intel's Advanced Vector Extensions (AVX) and Streaming SIMD Extensions 4 (SSE4) 4.2 on the Sandy Bridge processors of the time allowed for enhanced support of the kdb+ system. In June 2013, version 3.1 was released, with benchmarks up to 8 times faster than older versions. In March 2020, version 4.0 was released. New features included Multithreaded primitives, Intel Optane DC persistent memory support and Data at Rest Encryption.

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  Database application

  A database application is a computer program whose primary purpose is retrieving information from a computerized database. From here, information can be inserted, modified or deleted which is subsequently conveyed back into the database. Early examples of database applications were accounting systems and airline reservations systems, such as SABRE, developed starting in 1957. A characteristic of modern database applications is that they facilitate simultaneous updates and queries from multiple users. Systems in the 1970s might have accomplished this by having each user in front of a 3270 terminal to a mainframe computer. By the mid-1980s it was becoming more common to give each user a personal computer and have a program running on that PC that is connected to a database server. Information would be pulled from the database, transmitted over a network, and then arranged, graphed, or otherwise formatted by the program running on the PC. Starting in the mid-1990s it became more common to build database applications with a Web interface. Rather than develop custom software to run on a user's PC, the user would use the same Web browser program for every application. A database application with a Web interface had the advantage that it could be used on devices of different sizes, with different hardware, and with different operating systems. Examples of early database applications with Web interfaces include amazon.com, which used the Oracle relational database management system, the photo.net online community, whose implementation on top of Oracle was described in the book Database-Backed Web Sites (Ziff-Davis Press; May 1997), and eBay, also running Oracle. Electronic medical records are referred to on emrexperts.com, in December 2010, as "a software database application". A 2005 O'Reilly book uses the term in its title: Database Applications and the Web. Some of the most complex database applications remain accounting systems, such as SAP, which may contain thousands of tables in only a single module. Many of today's most widely used computer systems are database applications, for example, Facebook, which was built on top of MySQL. The etymology of the phrase "database application" comes from the practice of dividing computer software into systems programs, such as the operating system, compilers, the file system, and tools such as the database management system, and application programs, such as a payroll check processor. On a standard PC running Microsoft Windows, for example, the Windows operating system contains all of the systems programs while games, word processors, spreadsheet programs, photo editing programs, etc. would be application programs. As "application" is short for "application program", "database application" is short for "database application program". Not every program that uses a database would typically be considered a "database application". For example, many physics experiments, e.g., the Large Hadron Collider, generate massive data sets that programs subsequently analyze. The data sets constitute a "database", though they are not typically managed with a standard relational database management system. The computer programs that analyze the data are primarily developed to answer hypotheses, not to put information back into the database and therefore the overall program would not be called a "database application". == Examples of database applications == Amazon Student Data CNN eBay Facebook Fandango Filemaker (Mac OS) LibreOffice Base Microsoft Access Oracle relational database SAP (Systems, Applications & Products in Data Processing) Ticketmaster Wikipedia Yelp YouTube Google MySQL

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  Image texture

  An image texture is the small-scale structure perceived on an image, based on the spatial arrangement of color or intensities. It can be quantified by a set of metrics calculated in image processing. Image texture metrics give us information about the whole image or selected regions. Image textures can be artificially created or found in natural scenes captured in an image. Image textures are one way that can be used to help in segmentation or classification of images. For more accurate segmentation the most useful features are spatial frequency and an average grey level. To analyze an image texture in computer graphics, there are two ways to approach the issue: structured approach and statistical approach. == Structured approach == A structured approach sees an image texture as a set of primitive texels in some regular or repeated pattern. This works well when analyzing artificial textures. To obtain a structured description a characterization of the spatial relationship of the texels is gathered by using Voronoi tessellation of the texels. == Statistical approach == A statistical approach sees an image texture as a quantitative measure of the arrangement of intensities in a region. In general this approach is easier to compute and is more widely used, since natural textures are made of patterns of irregular subelements. === Edge detection === The use of edge detection is to determine the number of edge pixels in a specified region, helps determine a characteristic of texture complexity. After edges have been found the direction of the edges can also be applied as a characteristic of texture and can be useful in determining patterns in the texture. These directions can be represented as an average or in a histogram. Consider a region with N pixels. the gradient-based edge detector is applied to this region by producing two outputs for each pixel p: the gradient magnitude Mag(p) and the gradient direction Dir(p). The edgeness per unit area can be defined by F e d g e n e s s = | { p | M a g ( p ) > T } | N {\displaystyle F_{edgeness}={\frac {|\{p|Mag(p)>T\}|}{N}}} for some threshold T. To include orientation with edgeness histograms for both gradient magnitude and gradient direction can be used. Hmag(R) denotes the normalized histogram of gradient magnitudes of region R, and Hdir(R) denotes the normalized histogram of gradient orientations of region R. Both are normalized according to the size NR Then F m a g , d i r = ( H m a g ( R ) , H d i r ( R ) ) {\displaystyle F_{mag,dir}=(H_{mag}(R),H_{dir}(R))} is a quantitative texture description of region R. === Co-occurrence matrices === The co-occurrence matrix captures numerical features of a texture using spatial relations of similar gray tones. Numerical features computed from the co-occurrence matrix can be used to represent, compare, and classify textures. The following are a subset of standard features derivable from a normalized co-occurrence matrix: A n g u l a r 2 n d M o m e n t = ∑ i ∑ j p [ i , j ] 2 C o n t r a s t = ∑ i = 1 N g ∑ j = 1 N g n 2 p [ i , j ] , where | i − j | = n C o r r e l a t i o n = ∑ i = 1 N g ∑ j = 1 N g ( i j ) p [ i , j ] − μ x μ y σ x σ y E n t r o p y = − ∑ i ∑ j p [ i , j ] l n ( p [ i , j ] ) {\displaystyle {\begin{aligned}Angular{\text{ }}2nd{\text{ }}Moment&=\sum _{i}\sum _{j}p[i,j]^{2}\\Contrast&=\sum _{i=1}^{Ng}\sum _{j=1}^{Ng}n^{2}p[i,j]{\text{, where }}|i-j|=n\\Correlation&={\frac {\sum _{i=1}^{Ng}\sum _{j=1}^{Ng}(ij)p[i,j]-\mu _{x}\mu _{y}}{\sigma _{x}\sigma _{y}}}\\Entropy&=-\sum _{i}\sum _{j}p[i,j]ln(p[i,j])\\\end{aligned}}} where p [ i , j ] {\displaystyle p[i,j]} is the [ i , j ] {\displaystyle [i,j]} th entry in a gray-tone spatial dependence matrix, and Ng is the number of distinct gray-levels in the quantized image. One negative aspect of the co-occurrence matrix is that the extracted features do not necessarily correspond to visual perception. It is used in dentistry for the objective evaluation of lesions [DOI: 10.1155/2020/8831161], treatment efficacy [DOI: 10.3390/ma13163614; DOI: 10.11607/jomi.5686; DOI: 10.3390/ma13173854; DOI: 10.3390/ma13132935] and bone reconstruction during healing [DOI: 10.5114/aoms.2013.33557; DOI: 10.1259/dmfr/22185098; EID: 2-s2.0-81455161223; DOI: 10.3390/ma13163649]. === Laws texture energy measures === Another approach is to use local masks to detect various types of texture features. Laws originally used four vectors representing texture features to create sixteen 2D masks from the outer products of the pairs of vectors. The four vectors and relevant features were as follows: L5 = [ +1 +4 6 +4 +1 ] (Level) E5 = [ -1 -2 0 +2 +1 ] (Edge) S5 = [ -1 0 2 0 -1 ] (Spot) R5 = [ +1 -4 6 -4 +1 ] (Ripple) To these 4, a fifth is sometimes added: W5 = [ -1 +2 0 -2 +1 ] (Wave) From Laws' 4 vectors, 16 5x5 "energy maps" are then filtered down to 9 in order to remove certain symmetric pairs. For instance, L5E5 measures vertical edge content and E5L5 measures horizontal edge content. The average of these two measures is the "edginess" of the content. The resulting 9 maps used by Laws are as follows: L5E5/E5L5 L5R5/R5L5 E5S5/S5E5 S5S5 R5R5 L5S5/S5L5 E5E5 E5R5/R5E5 S5R5/R5S5 Running each of these nine maps over an image to create a new image of the value of the origin ([2,2]) results in 9 "energy maps," or conceptually an image with each pixel associated with a vector of 9 texture attributes. === Autocorrelation and power spectrum === The autocorrelation function of an image can be used to detect repetitive patterns of textures. == Texture segmentation == The use of image texture can be used as a description for regions into segments. There are two main types of segmentation based on image texture, region based and boundary based. Though image texture is not a perfect measure for segmentation it is used along with other measures, such as color, that helps solve segmenting in image. === Region based === Attempts to group or cluster pixels based on texture properties. === Boundary based === Attempts to group or cluster pixels based on edges between pixels that come from different texture properties.

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  ISO/IEC JTC 1/SC 24

  ISO/IEC JTC 1/SC 24 Computer graphics, image processing and environmental data representation is a standardization subcommittee of the joint subcommittee ISO/IEC JTC 1 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of computer graphics, image processing, and environmental data representation. The international secretariat of ISO/IEC JTC 1/SC 24 is the British Standards Institute (BSI) located in the United Kingdom. == History == ISO/IEC JTC 1/SC 24 was formed in 1987 from ISO/TC 97 as a result of Resolution 21 at the ISO/IEC JTC 1 plenary. The group's origins began in computer graphics, the standardization of which was originally under ISO/IEC JTC 1/SC 21/WG 2. However, when ISO/IEC JTC 1/SC 24 was created, the standardization activity of ISO/IEC JTC 1/SC 21/WG 2 was carried over to the new subcommittee. The initial five working groups of ISO/IEC JTC 1/SC 24 were titled, “Architecture,” “Application programming interfaces,” “Metafiles and interfaces,” “Language bindings,” and “Validation, testing and registration.” The work of ISO/IEC JTC 1/SC 24 began with the Graphical Kernel System (GKS), which was adopted from ISO/IEC JTC 1/SC 21/WG 2. However, since GKS only addressed 2D functionality, attention turned to the standardization of 3D functionality. This resulted in two standards being published: GKS-3D in 1988 and PHIGS in 1989, both of which addressed 3D functionality. Since 1991, ISO/IEC JTC 1/SC 24 has held plenaries in a number of countries, including the Netherlands, Germany, United States, France, Canada, Japan, Sweden, Korea, United Kingdom, Australia, and Czech Republic. == Scope == The scope of ISO/IEC JTC 1/SC 24 is the “Standardization of interfaces for information technology based applications relating to”: Computer graphics Image processing Environmental data representation Support for the Mixed and Augmented Reality (MAR) Interaction with, and visual representation of, information Included are the following related areas: Modeling and simulation and related reference models Virtual reality with accompanying augmented reality/augmented virtuality aspects and related reference models Application program interfaces Functional specifications Representation models Interchange formats, encodings and their specifications, including metafiles Device interfaces Testing methods Registration procedures Presentation and support for creation of multimedia, hypermedia, and mixed reality documents Excluded are the following areas: Character and image coding Coding of multimedia, hypermedia, and mixed reality document interchange formats JTC 1 work in user system interfaces and document presentation ISO/TC 207 work on ISO 14000 environment management, ISO/TC 211 work on geographic information and geomatics Software environments as described by ISO/IEC JTC 1/SC 22 == Structure == ISO/IEC JTC 1/SC 24 is made up of four active working groups, each of which carries out specific tasks in standards development within the field of computer graphics, image processing and environmental data representation, together with ITU-T Study Group 16. As a response to changing standardization needs, working groups of ISO/IEC JTC 1/SC 24 can be disbanded if their area of work is no longer applicable, or established if new working areas arise. The focus of each working group is described in the group's terms of reference. Active working groups of ISO/IEC JTC 1/SC 24 are: == Collaborations == ISO/IEC JTC 1/SC 24 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC, in order to avoid conflicting or duplicative work. Organizations internal to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: ISO/IEC JTC 1/WG 7, Sensor Networks ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 32, Data management and interchange ISO/TAG 14, Imagery and technology ISO/TC 130, Graphic Technology ISO/TC 184/SC 4, Industrial data ISO/TC 211, Geographic information/Geomatics ISO/TC 215, Health informatics IEC TC 100, Audio, video and multimedia system and equipment Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: Defence Geospatial Information Working Group (DGIWG) Digital Imaging and Communications in Medicine (DICOM) International Hydrographic Organization (IHO) The Khronos Group NATO - Joint Intelligence Surveillance and Reconnaissance Capability Group (JISRCG) OMG Robotics DTF Open CGM Open Geospatial Consortium (OGC) SEDRIS Organization Simulation Interoperability Standards Organization (SISO) US National Imagery Transmission Format Standard (NITFS) Technical Board (US NTB) Web3D Consortium World Intellectual Property Organization (WIPO) World Wide Web Consortium (W3C) == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 11 "P" (participating) members of ISO/IEC JTC 1/SC 24 are: Australia, China, Egypt, France, India, Japan, Republic of Korea, Portugal, Russian Federation, United Kingdom, and United States. The 22 "O" (observer) members of ISO/IEC JTC 1/SC 24 are: Argentina, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Cuba, Czech Republic, Finland, Ghana, Hungary, Iceland, Indonesia, Islamic Republic of Iran, Italy, Kazakhstan, Malaysia, Poland, Romania, Serbia, Slovakia, Switzerland, and Thailand. == Published standards == ISO/IEC JTC 1/SC 24 currently has 80 published standards under their direct responsibility within the field of computer graphics, image processing, and environmental data representation, including:

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  ParkMobile

  ParkMobile is a mobile and web app providing parking payments in North America. Headquartered in Atlanta, Georgia, users can pay for on-street and off-street parking via app on their smartphone, web browser, or through calling a phone number. ParkMobile also offers parking reservations at stadiums or venues for concerts and sporting events, and in metro area garages. == History == ParkMobile was founded in the United States in 2008 by Albert Bogaard after originally starting in the Netherlands. The initial product served only zone (on-demand) parkers and payment for the parking spot was made via a phone call through an IVR system. In 2009, the ParkMobile app was released and the product launched in its first city, Grand Rapids, Michigan. Parking payments have since been accepted through a user's account by connecting a credit card. ParkMobile deployed in Washington, D.C., in 2011. As of 2023, ParkMobile now has over 50 million users. Parking reservations were introduced in 2017, allowing users to reserve parking in advance. In 2018, the company recapitalized with BMW as the shareholder. ParkMobile was then acquired by a joint venture with BMW and Daimler. Under this joint venture, ParkMobile parking payment functionality was available and integrated with BMW's navigation system in many of its 2018 models. EasyPark Group, the Swedish-based parking solutions company, acquired ParkMobile in 2021 and is the current owner rebranded as Arrive. In 2022, ParkMobile launched in the City of Boston with a city-wide parking app, ParkBoston, powered by ParkMobile. == Operations == === Products === ParkMobile's product offerings include zone (on-demand) parking payments, parking reservations, and a self-service reporting engine. Zone parking is the company's most widely used service. Users can use the app on their smartphone to pay parking fees. In 2017, ParkMobile began offering parking reservations. The service is provided in addition to on-demand parking options at stadiums and venues, as well as metro area parking garages. After launching the reservations feature, ParkMobile became the first mobile parking app provider in North America to have a consolidated app with both on-demand and reservations parking in one. ParkMobile 360, the company's self-service management and reporting platform for operators, launched in 2018. It is a web-based application for parking operators to manage parking inventory, adjust rates, create special parking events, and track analytics. In 2020, ParkMobile began offering an option to pay for parking with Google through integrating the ParkMobile experience with Google Maps In 2021, ParkMobile launched its web application, allowing users to complete their parking transactions directly from the mobile website without having to download the app or have an account. ParkMobile integrates with parking gate equipment so customers can use their app to pay for parking and scan to enter and exit the garage. === Locations === ParkMobile has over 50 million users across the United States, Canada, and Puerto Rico. The app is available in over 550 cities in the U.S. and over 150 colleges and universities. == Controversies == === Predatory towing and excessive ticketing === Since all paid parking sessions from a single supplier are able to be viewed together, the ease of viewing and enforcing parking violations has caused controversy. Parking Enforcement Services in Birmingham, Alabama, has been the subject complaints by users of the ParkMobile app who had paid for a parking session and still had their vehicle towed. Customers often use old or expired license plates and forget to update to the correct number, or mistype when entering their information into the ParkMobile app. The complaints are that the towing companies offer no lenience for these mistakes. They return to their car as the session expires, and find their car has been towed. Additionally, other municipality across the country have received complaints about excessive parking ticket issuing when inputting their information incorrectly in the ParkMobile app. In Stone Harbor, New Jersey, parking ticket violations increased by over 1,600% from the previous year since launching with the ParkMobile app. Police officers refute complaints of being "too strict" on writing tickets by admitting the ParkMobile system allows officers to "more seamlessly enforce" the city's parking laws. === Data security breach === In March 2021, ParkMobile suffered a cybersecurity incident "linked to a vulnerability in a third-party software," potentially exposing users' email addresses, phone numbers, and license plate numbers. ParkMobile responded by launching an investigation and notifying law enforcement authorities and affected municipalities. The investigation concluded "no sensitive data or Payment Card Information was affected" but ParkMobile confirmed that basic account information, such as license plate numbers and possibly email addresses or phone numbers, was accessed.

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  Aikuma

  Aikuma is an Android app for collecting speech recordings with time-aligned translations. The app includes a text-free interface for consecutive interpretation, designed for users who are not literate. The Aikuma won Grand Prize in the Open Source Software World Challenge (2013). == Name == Aikuma means "meeting place" in Usarufa, a Papuan language where this software was first used in 2012. == History == Aikuma was developed with sponsorship from the National Science Foundation, including a $101,501 (US) project, "to use mobile telephones to collect larger amounts of data on undocumented endangered languages than would never be possible through usual fieldwork." Aikuma and its modified version (Lig-Aikuma) have been used for collecting substantial quantities of audio in remote indigenous villages. A modified version of the app, called Lig-Aikuma, has been developed at the Université Grenoble Alpes (LIG laboratory) and implements new features such as elicitation of speech from text, images and videos. == Similar Software == Lingua Libre is an online collaborative project and tool by the Wikimedia France association, which can be used as a tool for Language Preservation. Lingua Libre enables to record words, phrases, or sentences of any language, oral (audio recording) or signed (video recording). It is a highly efficient method to record endangered languages since up to 1000 words can be recorded per hour. All the content is under Free License, and speakers of minority languages are encouraged to record their own dialects.

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  Fake nude photography

  Fake nude photography is the creation of nude photographs designed to appear as genuine nudes of an individual. The motivations for the creation of these modified photographs include curiosity, sexual gratification, the stigmatization or embarrassment of the subject, and commercial gain, such as through the sale of the photographs via pornographic websites. Fakes can be created using image editing software or through machine learning. Fake images created using the latter method are called deepfakes. == History == Magazines such as Celebrity Skin published non-fake paparazzi shots and illicitly obtained nude photos, showing there was a market for such images. Subsequently, some websites hosted fake nude or pornographic photos of celebrities, which are sometimes referred to as celebrity fakes. In the 1990s and 2000s, fake nude images of celebrities proliferated on Usenet and on websites, leading to campaigns to take legal action against the creators of the images and websites dedicated to determining the veracity of nude photos. "Deepfakes", which use artificial neural networks to superimpose one person's face into an image or video of someone else, were popularized in the late 2010s, leading to concerns about the technology's use in fake news and revenge porn. Fake nude photography is sometimes confused with Deepfake pornography, but the two are distinct. Fake nude photography typically starts with human-made non-sexual images, and merely makes it appear that the people in them are nude (but not having sex). Deepfake pornography typically starts with human-made sexual (pornographic) images or videos, and alters the actors' facial features to make the participants in the sexual act look like someone else. === DeepNude === In June 2019, a downloadable Windows and Linux application called DeepNude was released which used a Generative Adversarial Network to remove clothing from images of women. The images it produced were typically not pornographic, merely nude. Because there were more images of nude women than men available to its creator, the images it produced were all female, even when the original was male. The app had both a paid and unpaid version. A few days later, on June 27, the creators removed the application and refunded consumers, although various copies of the app, both free and for charge, continue to exist. On GitHub, the open-source version of this program called "open-deepnude" was deleted. The open-source version had the advantage of allowing it to be trained on a larger dataset of nude images to increase the resulting nude image's accuracy level. A successor free software application, Dreamtime, was later released, and some copies of it remain available, though some have been suppressed. === Deepfake Telegram Bot === In July 2019 a deepfake bot service was launched on messaging app Telegram that used AI technology to create nude images of women. The service was free and enabled users to submit photos and receive manipulated nude images within minutes. The service was connected to seven Telegram channels, including the main channel that hosts the bot, technical support, and image sharing channels. While the total number of users was unknown, the main channel had over 45,000 members. As of July 2020, it is estimated that approximately 24,000 manipulated images had been shared across the image sharing channels. === Nudify websites === By late 2024, most ways to produce nude images from photographs of clothed people were accessible at websites rather than in apps, and required payment. == Purposes == The reasons for the creation of nude photos may range from a need to discredit the target publicly, personal hatred for the target, or the promise of pecuniary gains for such work on the part of the creator of such photos. Fake nude photos often target prominent figures such as businesspeople or politicians. == Notable cases == In 2010, 97 people were arrested in Korea after spreading fake nude pictures of the group Girls' Generation on the internet. In 2011, a 53-year-old Incheon man was arrested after spreading more fake pictures of the same group. In 2012, South Korean police identified 157 Korean artists of whom fake nudes were circulating. In 2012, when Liu Yifei's fake nude photography released on the network, Liu Yifei Red Star Land Company declared a legal search to find out who created and released the photos. In the same year, Chinese actor Huang Xiaoming released nude photos that sparked public controversy, but they were ultimately proven to be real pictures. In 2014, supermodel Kate Upton threatened to sue a website for posting her fake nude photos. Previously, in 2011, this page was threatened by Taylor Swift. In November 2014, singer Rain was angry because of a fake nude photo that spread throughout the internet. Information reveals that: "Rain's nude photo was released from Kim Tae-hee's lost phone." Rain's label, Cube Entertainment, stated that the person in the nude photo is not Rain and the company has since stated that it will take strict legal action against those who post photos together with false comments. In July 2018, Seoul police launched an investigation after a fake nude photo of President Moon Jae-in was posted on the website of the Korean radical feminist group WOMAD. In early 2019, Alexandria Ocasio-Cortez, a Democratic politician, was berated by other political parties over a fake nude photo of her in the bathroom. The picture created a huge wave of media controversy in the United States. == Methods == Fake nude images can be created using image editing software or neural network applications. There are two basic methods: Combine and superimpose existing images onto source images, adding the face of the subject onto a nude model. Remove clothes from the source image to make it look like a nude photo. == Impact == Images of this type may have a negative psychological impact on the victims and may be used for extortion purposes.

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  Operational image

  An operational image, also known as operative image, is an image that serves a functional, rather than aesthetic, purpose. Operational images are not intended to be viewed by people as representations of the real world; they are created to be used as instruments in performing some task or operation, often by machine automation. Operational images are used in a wide variety of applications, such as weapons targeting and guidance systems, and assisting surgeons performing robot-assisted surgery. The term "operational image" was first coined in 2000 by German filmmaker Harun Farocki in the first part of his three-part audiovisual installation, Eye/Machine. Farocki's installation included operational images used by militaries, such as weapons guidance and targeting systems. Eye/Machine featured images shown to the public by the United States military from the cameras used by laser-guided missiles in the Gulf War. Farocki defined operational images as "Images without a social goal, not for edification, not for reflection," and that they "do not represent an object, but rather are part of an operation." According to Volker Pantenburg, operational images are more accurately characterized as "visualizations of data". He describes operational images as a "working image" or an image that "performs work". Operational images are ubiquitous in modern society, used for a variety of military and non-military applications, such as inspecting sewer piping, and assisting surgeons performing robotic surgery.

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