AI For Business Strategy Mit

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  Automated essay scoring

  Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a form of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades, for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification. Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost. The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test). == History == Most historical summaries of AES trace the origins of the field to the work of Ellis Batten Page. In 1966, he argued for the possibility of scoring essays by computer, and in 1968 he published his successful work with a program called Project Essay Grade (PEG). Using the technology of that time, computerized essay scoring would not have been cost-effective, so Page abated his efforts for about two decades. Eventually, Page sold PEG to Measurement Incorporated. By 1990, desktop computers had become so powerful and so widespread that AES was a practical possibility. As early as 1982, a UNIX program called Writer's Workbench was able to offer punctuation, spelling and grammar advice. In collaboration with several companies (notably Educational Testing Service), Page updated PEG and ran some successful trials in the early 1990s. Peter Foltz and Thomas Landauer developed a system using a scoring engine called the Intelligent Essay Assessor (IEA). IEA was first used to score essays in 1997 for their undergraduate courses. It is now a product from Pearson Educational Technologies and used for scoring within a number of commercial products and state and national exams. IntelliMetric is Vantage Learning's AES engine. Its development began in 1996. It was first used commercially to score essays in 1998. Educational Testing Service offers "e-rater", an automated essay scoring program. It was first used commercially in February 1999. Jill Burstein was the team leader in its development. ETS's Criterion Online Writing Evaluation Service uses the e-rater engine to provide both scores and targeted feedback. Lawrence Rudner has done some work with Bayesian scoring, and developed a system called BETSY (Bayesian Essay Test Scoring sYstem). Some of his results have been published in print or online, but no commercial system incorporates BETSY as yet. Under the leadership of Howard Mitzel and Sue Lottridge, Pacific Metrics developed a constructed response automated scoring engine, CRASE. Currently utilized by several state departments of education and in a U.S. Department of Education-funded Enhanced Assessment Grant, Pacific Metrics’ technology has been used in large-scale formative and summative assessment environments since 2007. Measurement Inc. acquired the rights to PEG in 2002 and has continued to develop it. In 2012, the Hewlett Foundation sponsored a competition on Kaggle called the Automated Student Assessment Prize (ASAP). 201 challenge participants attempted to predict, using AES, the scores that human raters would give to thousands of essays written to eight different prompts. The intent was to demonstrate that AES can be as reliable as human raters, or more so. The competition also hosted a separate demonstration among nine AES vendors on a subset of the ASAP data. Although the investigators reported that the automated essay scoring was as reliable as human scoring, this claim was not substantiated by any statistical tests because some of the vendors required that no such tests be performed as a precondition for their participation. Moreover, the claim that the Hewlett Study demonstrated that AES can be as reliable as human raters has since been strongly contested, including by Randy E. Bennett, the Norman O. Frederiksen Chair in Assessment Innovation at the Educational Testing Service. Some of the major criticisms of the study have been that five of the eight datasets consisted of paragraphs rather than essays, four of the eight data sets were graded by human readers for content only rather than for writing ability, and that rather than measuring human readers and the AES machines against the "true score", the average of the two readers' scores, the study employed an artificial construct, the "resolved score", which in four datasets consisted of the higher of the two human scores if there was a disagreement. This last practice, in particular, gave the machines an unfair advantage by allowing them to round up for these datasets. In 1966, Page hypothesized that, in the future, the computer-based judge will be better correlated with each human judge than the other human judges are. Despite criticizing the applicability of this approach to essay marking in general, this hypothesis was supported for marking free text answers to short questions, such as those typical of the British GCSE system. Results of supervised learning demonstrate that the automatic systems perform well when marking by different human teachers is in good agreement. Unsupervised clustering of answers showed that excellent papers and weak papers formed well-defined clusters, and the automated marking rule for these clusters worked well, whereas marks given by human teachers for the third cluster ('mixed') can be controversial, and the reliability of any assessment of works from the 'mixed' cluster can often be questioned (both human and computer-based). == Different dimensions of essay quality == According to a recent survey, modern AES systems try to score different dimensions of an essay's quality in order to provide feedback to users. These dimensions include the following items: Grammaticality: following grammar rules Usage: using of prepositions, word usage Mechanics: following rules for spelling, punctuation, capitalization Style: word choice, sentence structure variety Relevance: how relevant of the content to the prompt Organization: how well the essay is structured Development: development of ideas with examples Cohesion: appropriate use of transition phrases Coherence: appropriate transitions between ideas Thesis Clarity: clarity of the thesis Persuasiveness: convincingness of the major argument == Procedure == From the beginning, the basic procedure for AES has been to start with a training set of essays that have been carefully hand-scored. The program evaluates surface features of the text of each essay, such as the total number of words, the number of subordinate clauses, or the ratio of uppercase to lowercase letters—quantities that can be measured without any human insight. It then constructs a mathematical model that relates these quantities to the scores that the essays received. The same model is then applied to calculate scores of new essays. Recently, one such mathematical model was created by Isaac Persing and Vincent Ng. which not only evaluates essays on the above features, but also on their argument strength. It evaluates various features of the essay, such as the agreement level of the author and reasons for the same, adherence to the prompt's topic, locations of argument components (major claim, claim, premise), errors in the arguments, cohesion in the arguments among various other features. In contrast to the other models mentioned above, this model is closer in duplicating human insight while grading essays. Due to the growing popularity of deep neural networks, deep learning approaches have been adopted for automated essay scoring, generally obtaining superior results, often surpassing inter-human agreement levels. The various AES programs differ in what specific surface features they measure, how many essays are required in the training set, and most significantly in the mathematical modeling technique. Early attempts used linear regression. Modern systems may use linear regression or other machine learning techniques often in combination with other statistical techniques such as latent semantic analysis and Bayesian inference. The automated essay scoring task has also been studied in the cross-domain setting using machine learning models, where the models are trained on essays written for one prompt (topic) and tested on essays written for another prompt. Successful approaches in the cross-domain scenario are based on deep neural networks or models that combine deep and shallow features. == Criteria for success == Any method of a

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  ArcSoft ShowBiz

  ShowBiz is a video editor by ArcSoft for the Windows operating system. It can create VCD and DVDs and can also export to the formats AVI, MPEG, WMV, and MOV. ShowBiz also contains a DVD burning and menu building feature. As of 2003, it was one of the three most dominant bundled titles. == Reception == PC Magazine reviewer Jan Ozer states: "ArcSoft's ShowBiz has evolved into a competent editor that's generally more usable than Dazzle's MovieStar program, providing more configuration controls, better preview features, and a much greater range of fun effects." John Virata, senior editor of Digital Media Online, says in his three page review of ShowBiz DVD 2, "It is an easy editor to work with and has a logically laid out interface that takes you step by step through the video creation and DVD creation process"

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  Automation

  Automation describes a wide range of technologies that reduce human intervention in processes, mainly by predetermining decision criteria, subprocess relationships, and related actions, as well as embodying those predeterminations in machines. Automation has been achieved by various means including mechanical, hydraulic, pneumatic, electrical, electronic devices, and computers, usually in combination. Complicated systems, such as modern factories, airplanes, and ships typically use combinations of all of these techniques. The benefits of automation includes labor savings, reducing waste, savings in electricity costs, savings in material costs, and improvements to quality, accuracy, and precision. Automation includes the use of various equipment and control systems such as machinery, processes in factories, boilers, and heat-treating ovens, switching on telephone networks, steering, stabilization of ships, aircraft and other applications and vehicles with reduced human intervention. Examples range from a household thermostat controlling a boiler to a large industrial control system with tens of thousands of input measurements and output control signals. In the simplest type of an automatic control loop, a controller compares a measured value of a process with a desired set value and processes the resulting error signal to change some input to the process, in such a way that the process stays at its set point despite disturbances. This closed-loop control is an application of negative feedback to a system. The mathematical basis of control theory began in the 18th century and advanced rapidly in the 20th. The term automation, inspired by the earlier word automatic (coming from automaton), was not widely used before 1947, when Ford established an automation department. It was during this time that the industry was rapidly adopting feedback controllers, Technological advancements introduced in the 1930s revolutionized various industries significantly. The World Bank's World Development Report of 2019 shows evidence that the new industries and jobs in the technology sector outweigh the economic effects of workers being displaced by automation. Job losses and downward mobility blamed on automation have been cited as one of many factors in the resurgence of nationalist, protectionist and populist politics in the US, UK and France, among other countries since the 2010s. == History == === Early history === It was a preoccupation of the Greeks and Arabs (in the period between about 300 BC and about 1200 AD) to keep an accurate track of time. In Ptolemaic Egypt, about 270 BC, Ctesibius described a float regulator for a water clock, a device not unlike the ball and cock in a modern flush toilet. This was the earliest feedback-controlled mechanism. The appearance of the mechanical clock in the 14th century made the water clock and its feedback control system obsolete. The Persian Banū Mūsā brothers, in their Book of Ingenious Devices (850 AD), described a number of automatic controls. Two-step level controls for fluids, a form of discontinuous variable structure controls, were developed by the Banu Musa brothers. They also described a feedback controller. The design of feedback control systems up through the Industrial Revolution was by trial-and-error, together with a great deal of engineering intuition. It was not until the mid-19th century that the stability of feedback control systems was analyzed using mathematics, the formal language of automatic control theory. The centrifugal governor was invented by Christiaan Huygens in the seventeenth century, and used to adjust the gap between millstones. === Industrial Revolution in Western Europe === The introduction of prime movers, or self-driven machines advanced grain mills, furnaces, boilers, and the steam engine created a new requirement for automatic control systems including temperature regulators (invented in 1624; see Cornelius Drebbel), pressure regulators (1681), float regulators (1700) and speed control devices. Another control mechanism was used to tent the sails of windmills. It was patented by Edmund Lee in 1745. Also in 1745, Jacques de Vaucanson invented the first automated loom. Around 1800, Joseph Marie Jacquard created a punch-card system to program looms. In 1771 Richard Arkwright invented the first fully automated spinning mill driven by water power, known at the time as the water frame. An automatic flour mill was developed by Oliver Evans in 1785, making it the first completely automated industrial process. A centrifugal governor was used by Mr. Bunce of England in 1784 as part of a model steam crane. The centrifugal governor was adopted by James Watt for use on a steam engine in 1788 after Watt's partner Boulton saw one at a flour mill Boulton & Watt were building. The governor could not actually hold a set speed; the engine would assume a new constant speed in response to load changes. The governor was able to handle smaller variations such as those caused by fluctuating heat load to the boiler. Also, there was a tendency for oscillation whenever there was a speed change. As a consequence, engines equipped with this governor were not suitable for operations requiring constant speed, such as cotton spinning. Several improvements to the governor, plus improvements to valve cut-off timing on the steam engine, made the engine suitable for most industrial uses before the end of the 19th century. Advances in the steam engine stayed well ahead of science, both thermodynamics and control theory. The governor received relatively little scientific attention until James Clerk Maxwell published a paper that established the beginning of a theoretical basis for understanding control theory. === 20th century === Relay logic was introduced with factory electrification, which underwent rapid adaptation from 1900 through the 1920s. Central electric power stations were also undergoing rapid growth and the operation of new high-pressure boilers, steam turbines and electrical substations created a great demand for instruments and controls. Central control rooms became common in the 1920s, but as late as the early 1930s, most process controls were on-off. Operators typically monitored charts drawn by recorders that plotted data from instruments. To make corrections, operators manually opened or closed valves or turned switches on or off. Control rooms also used color-coded lights to send signals to workers in the plant to manually make certain changes. The development of the electronic amplifier during the 1920s, which was important for long-distance telephony, required a higher signal-to-noise ratio, which was solved by negative feedback noise cancellation. This and other telephony applications contributed to the control theory. In the 1940s and 1950s, German mathematician Irmgard Flügge-Lotz developed the theory of discontinuous automatic controls, which found military applications during the Second World War to fire control systems and aircraft navigation systems. Controllers, which were able to make calculated changes in response to deviations from a set point rather than on-off control, began being introduced in the 1930s. Controllers allowed manufacturing to continue showing productivity gains to offset the declining influence of factory electrification. Factory productivity was greatly increased by electrification in the 1920s. U.S. manufacturing productivity growth fell from 5.2%/yr 1919–29 to 2.76%/yr 1929–41. Alexander Field notes that spending on non-medical instruments increased significantly from 1929 to 1933 and remained strong thereafter. The First and Second World Wars saw major advancements in the field of mass communication and signal processing. Other key advances in automatic controls include differential equations, stability theory and system theory (1938), frequency domain analysis (1940), ship control (1950), and stochastic analysis (1941). Starting in 1958, various systems based on solid-state digital logic modules for hard-wired programmed logic controllers (the predecessors of programmable logic controllers [PLC]) emerged to replace electro-mechanical relay logic in industrial control systems for process control and automation, including early Telefunken/AEG Logistat, Siemens Simatic, Philips/Mullard/Valvo Norbit, BBC Sigmatronic, ACEC Logacec, Akkord Estacord, Krone Mibakron, Bistat, Datapac, Norlog, SSR, or Procontic systems. In 1959 Texaco's Port Arthur Refinery became the first chemical plant to use digital control. Conversion of factories to digital control began to spread rapidly in the 1970s as the price of computer hardware fell. === Significant applications === The automatic telephone switchboard was introduced in 1892 along with dial telephones. By 1929, 31.9% of the Bell system was automatic. Automatic telephone switching originally used vacuum tube amplifiers and electro-mechanical switches, which consumed a large amount of electricity. Call volume eve

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  Voice search

  Voice search, also called voice-enabled search, allows the user to use a voice to search the Internet, a website, or an app. In a broader definition, voice search includes open-domain keyword query on any information on the Internet, for example in Google Voice Search, Cortana, Siri and Amazon Echo. Voice search is often interactive, involving several rounds of interaction that allows a system to ask for clarification. Voice search is a type of dialog system. Voice search is not a replacement for typed search. Rather the search terms, experience and use cases can differ heavily depending on the input type. == Supported language == Language is the most essential factor for a system to understand, and provide the most accurate results of what the user searches. This covers across languages, dialects, and accents, as users want a voice assistant that both understands them and speaks to them understandably. While spoken and written languages differ, voice search should support natural spoken language instead of only transforming voice into text and doing a regular text search with the help speech recognition. For example, in typed search an eCommerce user can easily copy and paste an alphanumeric product code to search field, but when speaking the search terms can be very different, such as "show me the new Bluetooth headphones by Samsung". == How it works == The difference between text and voice search is not only the input type. The mechanism must include an automatic speech recognition (ASR) for input, but it can also include natural language understanding for natural spoken search queries such as "What's the population for the United States" It can include text-to-speech (TTS) or a regular display for output modalities. Users might sometimes be required to activate the search by using a wake word. Then, the search system will detect the language spoken by the user. It will then detect the keywords and context of the sentence. Lastly, the device will return results depending on its output. A device with a screen might display the results, while a device without a screen will speak them back to the searcher.

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  Granular computing

  Granular computing is an emerging computing paradigm of information processing that concerns the processing of complex information entities called "information granules", which arise in the process of data abstraction and derivation of knowledge from information or data. Generally speaking, information granules are collections of entities that usually originate at the numeric level and are arranged together due to their similarity, functional or physical adjacency, indistinguishability, coherency, or the like. At present, granular computing is more a theoretical perspective than a coherent set of methods or principles. As a theoretical perspective, it encourages an approach to data that recognizes and exploits the knowledge present in data at various levels of resolution or scales. In this sense, it encompasses all methods which provide flexibility and adaptability in the resolution at which knowledge or information is extracted and represented. == Types of granulation == As mentioned above, granular computing is not an algorithm or process; there is no particular method that is called "granular computing". It is rather an approach to looking at data that recognizes how different and interesting regularities in the data can appear at different levels of granularity, much as different features become salient in satellite images of greater or lesser resolution. On a low-resolution satellite image, for example, one might notice interesting cloud patterns representing cyclones or other large-scale weather phenomena, while in a higher-resolution image, one misses these large-scale atmospheric phenomena but instead notices smaller-scale phenomena, such as the interesting pattern that is the streets of Manhattan. The same is generally true of all data: At different resolutions or granularities, different features and relationships emerge. The aim of granular computing is to try to take advantage of this fact in designing more effective machine-learning and reasoning systems. There are several types of granularity that are often encountered in data mining and machine learning, and we review them below: === Value granulation (discretization/quantization) === One type of granulation is the quantization of variables. It is very common that in data mining or machine-learning applications the resolution of variables needs to be decreased in order to extract meaningful regularities. An example of this would be a variable such as "outside temperature" (temp), which in a given application might be recorded to several decimal places of precision (depending on the sensing apparatus). However, for purposes of extracting relationships between "outside temperature" and, say, "number of health-club applications" (club), it will generally be advantageous to quantize "outside temperature" into a smaller number of intervals. ==== Motivations ==== There are several interrelated reasons for granulating variables in this fashion: Based on prior domain knowledge, there is no expectation that minute variations in temperature (e.g., the difference between 80–80.7 °F (26.7–27.1 °C)) could have an influence on behaviors driving the number of health-club applications. For this reason, any "regularity" which our learning algorithms might detect at this level of resolution would have to be spurious, as an artifact of overfitting. By coarsening the temperature variable into intervals the difference between which we do anticipate (based on prior domain knowledge) might influence number of health-club applications, we eliminate the possibility of detecting these spurious patterns. Thus, in this case, reducing resolution is a method of controlling overfitting. By reducing the number of intervals in the temperature variable (i.e., increasing its grain size), we increase the amount of sample data indexed by each interval designation. Thus, by coarsening the variable, we increase sample sizes and achieve better statistical estimation. In this sense, increasing granularity provides an antidote to the so-called curse of dimensionality, which relates to the exponential decrease in statistical power with increase in number of dimensions or variable cardinality. Independent of prior domain knowledge, it is often the case that meaningful regularities (i.e., which can be detected by a given learning methodology, representational language, etc.) may exist at one level of resolution and not at another. For example, a simple learner or pattern recognition system may seek to extract regularities satisfying a conditional probability threshold such as p ( Y = y j | X = x i ) ≥ α . {\displaystyle p(Y=y_{j}|X=x_{i})\geq \alpha .} In the special case where α = 1 , {\displaystyle \alpha =1,} this recognition system is essentially detecting logical implication of the form X = x i → Y = y j {\displaystyle X=x_{i}\rightarrow Y=y_{j}} or, in words, "if X = x i , {\displaystyle X=x_{i},} then Y = y j {\displaystyle Y=y_{j}} ". The system's ability to recognize such implications (or, in general, conditional probabilities exceeding threshold) is partially contingent on the resolution with which the system analyzes the variables. As an example of this last point, consider the feature space shown to the right. The variables may each be regarded at two different resolutions. Variable X {\displaystyle X} may be regarded at a high (quaternary) resolution wherein it takes on the four values { x 1 , x 2 , x 3 , x 4 } {\displaystyle \{x_{1},x_{2},x_{3},x_{4}\}} or at a lower (binary) resolution wherein it takes on the two values { X 1 , X 2 } . {\displaystyle \{X_{1},X_{2}\}.} Similarly, variable Y {\displaystyle Y} may be regarded at a high (quaternary) resolution or at a lower (binary) resolution, where it takes on the values { y 1 , y 2 , y 3 , y 4 } {\displaystyle \{y_{1},y_{2},y_{3},y_{4}\}} or { Y 1 , Y 2 } , {\displaystyle \{Y_{1},Y_{2}\},} respectively. At the high resolution, there are no detectable implications of the form X = x i → Y = y j , {\displaystyle X=x_{i}\rightarrow Y=y_{j},} since every x i {\displaystyle x_{i}} is associated with more than one y j , {\displaystyle y_{j},} and thus, for all x i , {\displaystyle x_{i},} p ( Y = y j | X = x i ) < 1. {\displaystyle p(Y=y_{j}|X=x_{i})<1.} However, at the low (binary) variable resolution, two bilateral implications become detectable: X = X 1 ↔ Y = Y 1 {\displaystyle X=X_{1}\leftrightarrow Y=Y_{1}} and X = X 2 ↔ Y = Y 2 {\displaystyle X=X_{2}\leftrightarrow Y=Y_{2}} , since every X 1 {\displaystyle X_{1}} occurs iff Y 1 {\displaystyle Y_{1}} and X 2 {\displaystyle X_{2}} occurs iff Y 2 . {\displaystyle Y_{2}.} Thus, a pattern recognition system scanning for implications of this kind would find them at the binary variable resolution, but would fail to find them at the higher quaternary variable resolution. ==== Issues and methods ==== It is not feasible to exhaustively test all possible discretization resolutions on all variables in order to see which combination of resolutions yields interesting or significant results. Instead, the feature space must be preprocessed (often by an entropy analysis of some kind) so that some guidance can be given as to how the discretization process should proceed. Moreover, one cannot generally achieve good results by naively analyzing and discretizing each variable independently, since this may obliterate the very interactions that we had hoped to discover. A sample of papers that address the problem of variable discretization in general, and multiple-variable discretization in particular, is as follows: Chiu, Wong & Cheung (1991), Bay (2001), Liu et al. (2002), Wang & Liu (1998), Zighed, Rabaséda & Rakotomalala (1998), Catlett (1991), Dougherty, Kohavi & Sahami (1995), Monti & Cooper (1999), Fayyad & Irani (1993), Chiu, Cheung & Wong (1990), Nguyen & Nguyen (1998), Grzymala-Busse & Stefanowski (2001), Ting (1994), Ludl & Widmer (2000), Pfahringer (1995), An & Cercone (1999), Chiu & Cheung (1989), Chmielewski & Grzymala-Busse (1996), Lee & Shin (1994), Liu & Wellman (2002), Liu & Wellman (2004). === Variable granulation (clustering/aggregation/transformation) === Variable granulation is a term that could describe a variety of techniques, most of which are aimed at reducing dimensionality, redundancy, and storage requirements. We briefly describe some of the ideas here, and present pointers to the literature. ==== Variable transformation ==== A number of classical methods, such as principal component analysis, multidimensional scaling, factor analysis, and structural equation modeling, and their relatives, fall under the genus of "variable transformation." Also in this category are more modern areas of study such as dimensionality reduction, projection pursuit, and independent component analysis. The common goal of these methods in general is to find a representation of the data in terms of new variables, which are a linear or nonlinear transformation of the original variables, and in which important stati

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  Scientific Working Group – Imaging Technology

  The Scientific Working Group on Imaging Technology was convened by the Federal Bureau of Investigation in 1997 to provide guidance to law enforcement agencies and others in the criminal justice system regarding the best practices for photography, videography, and video and image analysis. This group was terminated in 2015. == History == As technology has advanced through the years, law enforcement has needed to stay abreast of emerging technological advances and use these in the investigation of crime. A factor that is considered when new technology is used in these investigations is the determination of whether the use of that new technology will be admissible in court. The judicial system in the United States currently has two standards used in the determination of admissibility of testimony regarding scientific evidence; the Daubert Standard and the Frye Standard. These standards guide the courts in the admissibility of testimony derived from the use of new technologies and scientific techniques. The Federal Bureau of Investigation (FBI), seeking to address possible admissibility issues with such testimony, established Scientific Working Groups starting with the Scientific Working Group on DNA Analysis and Methods (SWGDAM) in 1988. The goal of these groups is to open lines of communication between law enforcement agencies and forensic laboratories around the world while providing guidance on the use of new and innovative technologies and techniques. This guidance can lead to admissibility of evidence and/or testimony, provided proper methods in the collection of evidence and its analysis are employed. In 2009, the National Academy of Sciences released a report entitled, "Strengthening Forensic Science in the United States: A Path Forward." This report addresses many topics including challenges and disparities facing the forensic science community, standardization, certification of practitioners and accreditation of their respective entities, problems related to the interpretation of forensic evidence, the need for research, and the admission of forensic science evidence in litigation. This report mentions the Scientific Working Groups and their role in forensic science. The history of imaging technology (photography) can be said to extend back to the times of Chinese philosopher Mo-Ti (470-390 B.C.) who described the principles behind the precursor to the camera obscura. Since that time, advances in imaging technology include the discovery of chemical photographic processes in the 19th century and the use of electronic imaging technology that includes analog video cameras and digital video and still cameras. By the mid 1990s, it was apparent that technologically advanced camera systems such as these were being adopted for use in the criminal justice system. This led the FBI to convene a meeting of individuals working in the field of forensic imaging from federal, state, local, and foreign law enforcement, and the U.S. military, during the summer of 1997. As a result of this meeting, the Technical Working Group on Imaging Technology was formed from a core group of the meeting’s participants. This group later became the Scientific Working Group on Imaging Technology (SWGIT). Prior to the inception of SWGIT, some law enforcement agencies began adopting digital imaging technology. Due to the lack of guidelines or standards, some of these agencies attempted to replace all their film cameras with substandard digital cameras, only to find that the equipment they had purchased was not capable of accomplishing the mission for which they were intended. At that time only low resolution digital cameras were deemed affordable by some law enforcement agencies. Some of these agencies were forced to rethink their photography procedures and reverted to the use of film cameras or replaced their low-resolution digital cameras with higher quality, more expensive equipment. Also lacking at this early stage was guidance on how to store and archive digital image files. When SWGIT was formed, it was tasked with providing guidance to law enforcement and others in the criminal justice system by releasing documents that describe the best practices and guidelines for the use of imaging technology, to include these concerns and many others. This group was terminated in 2015. == SWGIT Function == During its existence, SWGIT provided information on the appropriate use of various imaging technologies including both established and new. This was accomplished through the release of documents such as the SWGIT Best Practices documents. As changes in technology occurred, these documents were updated. Over the course of its existence, SWGIT collaborated with other Scientific Working Groups to address imaging concerns within their respective disciplines. SWGIT published over 20 documents that dealt specifically with imaging technology. SWGIT also co-published documents with the Scientific Working Group on Digital Evidence (SWGDE) that had a component or components dealing with imaging technology. SWGIT also provided imaging technology guidance and input for documents from the Scientific Working Group on Friction Ridge Analysis, Study and Technology (SWGFAST), the Scientific Working Group for Forensic Document Examination (SWGDOC), and the Scientific Working Group on Shoeprint and Tire Tread Evidence (SWGTREAD). SWGIT assisted the American Society of Crime Lab Directors/Laboratory Accreditation Board (ASCLD/LAB) in the writing of definitions and standards for the accreditation of Digital and Multimedia Evidence sections of crime laboratories. In addition to releasing documents, SWGIT members disseminated best practices for law enforcement professionals where imaging technology was concerned. This was carried out by attending and lecturing at meetings and conferences of various forensic organizations that included: The American Academy of Forensic Sciences (AAFS) The International Association for Identification (IAI) The Law Enforcement and Emergency Services Video Association (LEVA) The American Society of Crime Lab Directors (ASCLD) The SWGIT membership consisted of approximately fifty scientists, photographers, instructors, and managers from more than two dozen federal, state, and local law enforcement agencies, as well as from the academic and research communities. The membership elected its officers from within. SWGIT was composed of the Executive Committee, four standing subcommittees, and ad hoc subcommittees appointed on an as-needed basis. The standing subcommittees were: Image Analysis, Forensic Photography, Video, and Outreach. This group was terminated in 2015. == Legal Proceedings == The following court cases have conducted Daubert v. Merrell Dow Pharm., Inc., 509 U.S. 579 (1993) hearings in which SWGIT best practice documents have been cited as accepted protocol, methodology, and as generally accepted techniques in the forensic community: U. S. v. Rudy Frabizio, U.S. District Court, Boston, MA, 2008 (Image Authentication) U.S. v. Nobumochi Furukawa, U.S. District Court, Minnesota, 2007 (Video Authentication) U.S. v. John Stroman, U.S. District Court, South Carolina, 2007 (Facial Comparison Analysis) State of Texas v. Daniel Day, Tarrant County Texas, 2005 (Camera Identification to Images) U.S. v. Marc Watzman, U.S. District Court, Northern Illinois, 2004 (Video Authentication) U.S. v. McKreith, U.S. District Court, Fort Lauderdale, FL, 2002 (Photo comparison of shirt) == Termination == This group was unfunded by the FBI in 2015.

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  Screenpal

  ScreenPal (formerly known as Screencast-O-Matic) is cross-platform screen capture and screen recording software originally developed in 2006. == History == The company was founded by AJ Gregory in 2006 as Screencast-O-Matic. The software includes features for screen recording, screenshot capture, video editing, image editing, and a video and image hosting service. It is available for Windows and Mac operating systems, and has mobile apps for iOS and Android. The company launched a video editor in 2015. It began offering free video and image hosting in 2019, with premium hosting options for subscribers. In 2023, it was rebranded as ScreenPal.

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  ArcSoft ShowBiz

  ShowBiz is a video editor by ArcSoft for the Windows operating system. It can create VCD and DVDs and can also export to the formats AVI, MPEG, WMV, and MOV. ShowBiz also contains a DVD burning and menu building feature. As of 2003, it was one of the three most dominant bundled titles. == Reception == PC Magazine reviewer Jan Ozer states: "ArcSoft's ShowBiz has evolved into a competent editor that's generally more usable than Dazzle's MovieStar program, providing more configuration controls, better preview features, and a much greater range of fun effects." John Virata, senior editor of Digital Media Online, says in his three page review of ShowBiz DVD 2, "It is an easy editor to work with and has a logically laid out interface that takes you step by step through the video creation and DVD creation process"

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  Channel (digital image)

  Color digital images are made of pixels, and pixels are made of combinations of primary colors represented by a series of code. A channel in this context is the grayscale image of the same size as a color image, made of just one of these primary colors. For instance, an image from a standard digital camera will have a red, green and blue channel. A grayscale image has just one channel. In geographic information systems, channels are often referred to as raster bands. Another closely related concept is feature maps, which are used in convolutional neural networks. == Overview == In the digital realm, there can be any number of conventional primary colors making up an image; a channel in this case is extended to be the grayscale image based on any such conventional primary color. By extension, a channel is any grayscale image of the same dimension as and associated with the original image. Channel is a conventional term used to refer to a certain component of an image. In reality, any image format can use any algorithm internally to store images. For instance, GIF images actually refer to the color in each pixel by an index number, which refers to a table where three color components are stored. However, regardless of how a specific format stores the images, discrete color channels can always be determined, as long as a final color image can be rendered. The concept of channels is extended beyond the visible spectrum in multispectral and hyperspectral imaging. In that context, each channel corresponds to a range of wavelengths and contains spectroscopic information. The channels can have multiple widths and ranges. Three main channel types (or color models) exist, and have respective strengths and weaknesses. === RGB images === An RGB image has three channels: red, green, and blue. RGB channels roughly follow the color receptors in the human eye, and are used in computer displays and image scanners. If the RGB image is 24-bit (the industry standard as of 2005), each channel has 8 bits, for red, green, and blue—in other words, the image is composed of three images (one for each channel), where each image can store discrete pixels with conventional brightness intensities between 0 and 255. If the RGB image is 48-bit (very high color-depth), each channel has 16-bit per pixel color, that is 16-bit red, green, and blue for each per pixel. ==== RGB color sample ==== Notice how the grey trees have similar brightness in all channels, the red dress is much brighter in the red channel than in the other two, and how the green part of the picture is shown much brighter in the green channel. === YUV === YUV images are an affine transformation of the RGB colorspace, originated in broadcasting. The Y channel correlates approximately with perceived intensity, whilst the U and V channels provide colour information. === CMYK === A CMYK image has four channels: cyan, magenta, yellow, and key (black). CMYK is the standard for print, where subtractive coloring is used. A 32-bit CMYK image (the industry standard as of 2005) is made of four 8-bit channels, one for cyan, one for magenta, one for yellow, and one for key color (typically is black). 64-bit storage for CMYK images (16-bit per channel) is not common, since CMYK is usually device-dependent, whereas RGB is the generic standard for device-independent storage. ==== CMYK color sample ==== === HSV === HSV, or hue saturation value, stores color information in three channels, just like RGB, but one channel is devoted to brightness (value), and the other two convey colour information. The value channel is similar to (but not exactly the same as) the CMYK black channel, or its negative. HSV is especially useful in lossy video compression, where loss of color information is less noticeable to the human eye. == Alpha channel == The alpha channel stores transparency information—the higher the value, the more opaque that pixel is. No camera or scanner measures transparency, although physical objects certainly can possess transparency, but the alpha channel is extremely useful for compositing digital images together. Bluescreen technology involves filming actors in front of a primary color background, then setting that color to transparent, and compositing it with a background. The GIF and PNG image formats use alpha channels on the World Wide Web to merge images on web pages so that they appear to have an arbitrary shape even on a non-uniform background. == Other channels == In 3D computer graphics, multiple channels are used for additional control over material rendering; e.g., controlling specularity and so on. == Bit depth == In digitizing images, the color channels are converted to numbers. Since images contain thousands of pixels, each with multiple channels, channels are usually encoded in as few bits as possible. Typical values are 8 bits per channel or 16 bits per channel. Indexed color effectively gets rid of channels altogether to get, for instance, 3 channels into 8 bits (GIF) or 16 bits. == Optimized channel sizes == Since the brain does not necessarily perceive distinctions in each channel to the same degree as in other channels, it is possible that differing the number of bits allocated to each channel will result in more optimal storage; in particular, for RGB images, compressing the blue channel the most and the red channel the least may be better than giving equal space to each. Among other techniques, lossy video compression uses chroma subsampling to reduce the bit depth in color channels (hue and saturation), while keeping all brightness information (value in HSV). 16-bit HiColor stores red and blue in 5 bits, and green in 6 bits.

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  YaDICs

  YaDICs is a program written to perform digital image correlation on 2D and 3D tomographic images. The program was designed to be both modular, by its plugin strategy and efficient, by it multithreading strategy. It incorporates different transformations (Global, Elastic, Local), optimizing strategy (Gauss-Newton, Steepest descent), Global and/or local shape functions (Rigid-body motions, homogeneous dilatations, flexural and Brazilian test models)... == Theoretical background == === Context === In solid mechanics, digital image correlation is a tool that allows to identify the displacement field to register a reference image (called herein fixed image) to images during an experiment (mobile image). For example, it is possible to observe the face of a specimen with a painted speckle on it in order to determine its displacement fields during a tensile test. Before the appearance of such methods, researchers usually used strain gauges to measure the mechanical state of the material but strain gauges only measure the strain on a point and don't allow to understand material with an heterogeneous behavior. One can obtain a full in plane strain tensor by derivation of the displacement fields. Many methods are based upon the optical flow. In fluid mechanics a similar method is used, called Particle Image Velocimetry (PIV); the algorithms are similar to those of DIC but it is impossible to ensure that the optical flow is conserved so a vast majority of the software used the normalized cross correlation metric. In mechanics the displacement or velocity fields are the only concern, registering images is just a side effect. There is another process called image registration using the same algorithms (on monomodal images) but where the goal is to register images and thereby identifying the displacement field is just a side effect. YaDICs uses the general principle of image registration with a particular attention to the displacement fields basis. === Image registration principle === YaDICs can be explained using the classical image registration framework: === Image registration general scheme === The common idea of image registration and digital image correlation is to find the transformation between a fixed image and a moving one for a given metric using an optimization scheme. While there are many methods to achieve such a goal, Yadics focuses on registering images with the same modality. The idea behind the creation of this software is to be able to process data that comes from a μ-tomograph; i.e.: data cube over 10003 voxels. With such a size it is not possible to use naive approach usually used in a two-dimensional context. In order to get sufficient performances OpenMP parallelism is used and data are not globally stored in memory. As an extensive description of the different algorithms is given in. === Sampling === Contrary to image registration, Digital Image Correlation targets the transformation, one wants to extracted the most accurate transformation from the two images and not just match the images. Yadics uses the whole image as a sampling grid: it is thus a total sampling. === Interpolator === It is possible to choose between bilinear interpolation and bicubic interpolation for the grey level evaluation at non integer coordinates. The bi-cubic interpolation is the recommended one. === Metrics === ==== Sum of squared differences (SSD) ==== The SSD is also known as mean squared error. The equation below defines the SSD metric: S S D ( μ , I F , I M ) = 1 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) 2 , {\displaystyle SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {1}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)^{2},} where I F {\displaystyle {\mathcal {I_{F}}}} is the fixed image, I M {\displaystyle {\mathcal {I_{M}}}} the moving one, Ω F {\displaystyle \Omega _{F}} the integration area | Ω F | {\displaystyle \left|\Omega _{F}\right|} the number of pi(vo)xels (cardinal) and T μ {\displaystyle {T}_{\mu }} the transformation parametrized by μ The transformation can be written as: T μ ( x ) = x + { Φ ( x ) } t { μ } . {\displaystyle T_{\mu }(x)=x+\left\{\Phi (x)\right\}^{t}\left\{\mu \right\}.} This metric is the main one used in the YaDICs as it works well with same modality images. One has to find the minimum of this metric ==== Normalized cross-correlation ==== The normalized cross-correlation (NCC) is used when one cannot assure the optical flow conservation; it happens in case of change of lighting or if particles disappear from the scene can occur in particle images velocimetry (PIV). The NCC is defined by: N C C ( μ , I F , I M ) = ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) ( I M ( T μ ( x i ) ) − I M ¯ ) ∑ x i ∈ Ω F ( I F ( x i ) − I F ¯ ) 2 ∑ x i ∈ Ω F ( I M ( T μ ( x i ) ) − I M ¯ ) 2 , {\displaystyle NCC(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})={\dfrac {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)}{\sqrt {\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\overline {\mathcal {I_{F}}}}\right)^{2}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{M}}}({T}_{\mu }(x_{i}))-{\overline {\mathcal {I_{M}}}}\right)^{2}}}},} where I F ¯ {\displaystyle {\overline {\mathcal {I_{F}}}}} and I M ¯ {\displaystyle {\overline {\mathcal {I_{M}}}}} are the mean values of the fixed and mobile images. This metric is only used to find local translation in Yadics. This metric with translation transform can be solved using cross-correlation methods, which are non iterative and can be accelerated using Fast Fourier Transform . === Classification of transformations === There are three categories of parametrization: elastic, global and local transformation. The elastic transformations respect the partition of unity, there are no holes created or surfaces counted several times. This is commonly used in Image Registration by the use of B-Spline functions and in solid mechanics with finite element basis. The global transformations are defined on the whole picture using rigid body or affine transformation (which is equivalent to homogeneous strain transformation). More complex transformations can be defined such as mechanically based one. These transformations have been used for stress intensity factor identification by and for rod strain by. The local transformation can be considered as the same global transformation defined on several Zone Of Interest (ZOI) of the fixed image. ==== Global ==== Several global transforms have been implemented: Rigid and homogeneous (Tx,Ty,Rz in 2D; Tx,Ty,Tz,Rx,Ry,Rz,Exx,Eyy,Ezz,Eyz,Exz,Exy in 3D) Brazilian (Only in 2D), Dynamic Flexion, ==== Elastic ==== First-order quadrangular finite elements Q4P1 are used in Yadics. ===== Local ===== Every global transform can be used on a local mesh. === Optimization === The YaDICs optimization process follows a gradient descent scheme. The first step is to compute the gradient of the metric regarding the transform parameters ∂ S S D ( μ , I F , I M ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ∂ I M ( T μ ( x i ) ∂ μ = 2 | Ω F | ∑ x i ∈ Ω F ( I F ( x i ) − I M ( T μ ( x i ) ) ) ( ∂ T μ ( x i ) ∂ μ ) t ∂ I M ( T μ ( x i ) ) ∂ x {\displaystyle {\begin{array}{lcl}{\dfrac {\partial SSD(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right){\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i})}{\partial \mu }}\\&=&{\dfrac {2}{\left|\Omega _{F}\right|}}\sum _{x_{i}\in \Omega _{F}}\left({\mathcal {I_{F}}}(x_{i})-{\mathcal {I_{M}}}({T}_{\mu }(x_{i}))\right)\left({\dfrac {\partial {T}_{\mu }(x_{i})}{\partial \mu }}\right)^{t}{\dfrac {\partial {\mathcal {I_{M}}}({T}_{\mu }(x_{i}))}{\partial x}}\\\end{array}}} ==== Gradient method ==== Once the metric gradient has been computed, one has to find an optimization strategy The gradient method principle is explained below: μ k + 1 = μ k + α k d k {\displaystyle \mu _{k+1}=\mu _{k}+\alpha _{k}d_{k}} The gradient step can be constant or updated at every iteration. d k = − γ k ∂ C ( μ , I F , I M ) ∂ μ {\displaystyle d_{k}=-\gamma _{k}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}} , γ k {\displaystyle \gamma _{k}} allows one to choose between the following methods : γ k {\displaystyle \gamma _{k}} ⟹ {\displaystyle \Longrightarrow } steepest descent, γ k = [ ∂ C ( μ , I F , I M ) ∂ μ ∂ C ( μ , I F , I M ) ∂ μ t ] − 1 {\displaystyle \gamma _{k}=\left[{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}{\dfrac {\partial {\mathcal {C}}(\mu ,{\mathcal {I_{F}}},{\mathcal {I_{M}}})}{\partial \mu }}^{t}\right]^{-1}} ⟹ {\displaystyle \Longrightarrow } Gauss-Newto

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  DoorDash

  DoorDash, Inc. is an American company operating online food ordering and food delivery. It trades under the symbol DASH. With a 56% market share, DoorDash is the largest food delivery platform in the United States. It also has a 60% market share in the convenience delivery category. As of December 31, 2020, the platform was used by 450,000 merchants, 20 million consumers, and had over one million delivery couriers. Founded by Tony Xu, Andy Fang, Stanley Tang and Evan Moore, DoorDash made its debut on the Fortune 500 list in 2024, ranking No. 443. DoorDash has been sued for or held legally liable for withholding tips, reducing tip transparency, antitrust price manipulation, listing restaurants without permission, misclassifying workers, withholding sick time, and illegally selling personal data. As of April 2026, DoorDash operates in the United States (including Puerto Rico), Canada, Australia, and New Zealand. Through its subsidiaries Deliveroo and Wolt, the company also operates across Europe, as well as in Azerbaijan, Georgia, Israel, Kazakhstan, Kuwait, and the United Arab Emirates. == History == In January 2013, Stanford University students Tony Xu, Stanley Tang, Andy Fang and Evan Moore launched PaloAltoDelivery.com in Palo Alto, California. In the summer of 2013, it received US$120,000 in seed money from Y Combinator in exchange for a 7% stake. It incorporated as DoorDash in June 2013. DoorDash's first partnership with a fast food burger restaurant chain was in April 2016, when it partnered with CKE Restaurants, parent company of Carl's Jr. and Hardee's, for food delivery. In December 2017, DoorDash announced its partnership with Wendy's for delivery from its restaurants. In December 2018, DoorDash overtook Uber Eats to hold the second position in total US food delivery sales, behind GrubHub. By March 2019, it had exceeded GrubHub in total sales, at 27.6% of the on-demand delivery market. By early 2019, DoorDash was the largest food delivery provider in the U.S., as measured by consumer spending. In October 2019, DoorDash opened its first ghost kitchen, DoorDash Kitchen, in Redwood City, California, with four restaurants operating at the location. By June 2020, DoorDash had raised more than $2.5 billion over several financing rounds from investors including Y Combinator, Charles River Ventures, SV Angel, Khosla Ventures, Sequoia Capital, SoftBank Group, GIC, and Kleiner Perkins. DoorDash announced a partnership with KFC in September 2020, followed by Taco Bell in October 2020. In November 2020, DoorDash announced the opening of its first physical restaurant location, partnering up with Bay Area restaurant Burma Bites to offer delivery and pick-up orders. In December 2020, it became a public company via an initial public offering, raising $3.37 billion. In November 2021, DoorDash acquired Finland's Wolt for €7bn. In August 2022, DoorDash announced it would end its partnership with Walmart in September, ending the companies' cooperation agreement from 2018. In November 2022, DoorDash announced plans to lay off 1,250 corporate employees, or about six percent of its workforce, to rein in expenses. In June 2023, DoorDash announced it would give its drivers the option of earning an hourly minimum wage instead of being paid per delivery. However, drivers are only paid hourly when on an active delivery. In September 2023, the company transferred its stock listing from the New York Stock Exchange to the Nasdaq. On December 18, 2023, DoorDash was added to the Nasdaq-100 index. In March 2025, DoorDash announced a partnership with Klarna, a Buy Now, Pay Later (BNPL) service, letting customers schedule small payments over a set period of time. DoorDash received widespread criticism from this decision, including internet mockery, given concerns about the increase of household debt in America. In 2025, DoorDash acquired the UK-based delivery service Deliveroo for $3.88 billion. The combined company operates in 40 countries and serves 50 million users monthly. In September 2025, DoorDash and Ace Hardware (the largest hardware cooperative) announced their partnership to offer delivery for home use products from over 4,000 Ace locations. == Lawsuits against DoorDash == === 2017 class-action lawsuit for misclassifying workers === In 2017, a class-action lawsuit was filed against DoorDash for allegedly misclassifying delivery drivers in California and Massachusetts as independent contractors. In 2022, a tentative settlement was reached in which DoorDash would pay $100 million total, with $61 million going to over 900,000 drivers, paying out just over $130 per driver, and $28 million for the lawyers. Gizmodo criticized the settlement, noting that the $413 million that DoorDash CEO Tony Xu received the previous year was one of the largest CEO compensation packages of all time. === 2019 data breach lawsuit === On May 4, 2019, DoorDash confirmed 4.9 million customers, delivery workers and merchants had sensitive information stolen via a data breach. Those who joined the platform after April 5, 2018, were unaffected by the breach. A class-action lawsuit for the breach was filed against DoorDash in October 2019. === Withholding of tips and subsequent class-action lawsuits === In July 2019, the company's tipping policy was criticized by The New York Times, and later The Verge and Vox and Gothamist. Drivers receive a guaranteed minimum per order that is paid by DoorDash by default. When a customer added a tip, instead of going directly to the driver, it first went to the company to cover the guaranteed minimum. Drivers then only directly received the part of the tip that exceeded the guaranteed minimum per order. In January 2020, it was reported that DoorDash had lied about skimming tips from its drivers, causing them to earn an average of $1.45 an hour after expenses, and that after the company had allegedly overhauled its tipping system, DoorDash was still manipulating per-delivery payouts at the expense of drivers. A DoorDash customer filed a class action lawsuit against the company for its "materially false and misleading" tipping policy. The case was referred to arbitration in August 2020. Under pressure, the company revised its policy. The company settled a lawsuit with District of Columbia Attorney General Karl Racine for $2.5 million, with funds going to deliverers, the government, and to charity. ==== 2021 driver strike for tip transparency ==== In July 2021, DoorDash drivers went on strike to protest lack of tip transparency and to ask for higher pay. At the time of the strike, and, as of June 2022, DoorDash did not allow drivers to see the full tip amounts prior to accepting a delivery in the app. If customers tip over a set amount for the order total, Doordash hides a portion of the tip until the delivery is complete. The strike occurred after DoorDash rewrote its code to cut off access to Para, a third-party app that drivers had been using to see the full tip amounts. ==== 2025 class-action lawsuit settlement ==== In 2025, DoorDash agreed to pay around $17 million for "misleading both consumers and delivery workers" with tips being docked from drivers' pay instead of directly going to drivers. === 2020 antitrust litigation === In April 2020, in the case of Davitashvili v. GrubHub Inc. DoorDash, Grubhub, Postmates, and Uber Eats were accused of monopolistic power by only listing restaurants on its apps if the restaurant owners signed contracts which include clauses that require prices be the same for dine-in customers as for customers receiving delivery. The plaintiffs stated that this arrangement increases the cost for dine-in customers, as they are required to subsidize the cost of delivery; and that the apps charge "exorbitant" fees, which range from 13% to 40% of revenue, while the average restaurant's profit ranges from 3% to 9% of revenue. The lawsuit seeks treble damages, including for overcharges, since April 14, 2016, for dine-in and delivery customers in the United States at restaurants using the defendants’ delivery apps. Although several preliminary documents in the case have now been filed, a trial date has not yet been set. === Litigation for illegal unauthorized restaurant listing === In May 2021, DoorDash was criticized for unauthorized listings of restaurants who had not given permission to appear on the app. The company was sued by Lona's Lil Eats in St. Louis, with the lawsuit claiming that DoorDash had listed them without permission, then prevented any orders to the restaurant from going through and redirecting customers to other restaurants instead, because Lona's was "too far away," when in reality it had not paid DoorDash a fee for listing. This aspect of DoorDash's business practice is illegal in California. === 2021 lawsuit by the city of Chicago === In August 2021, the city of Chicago sued DoorDash and GrubHub. According to Chicago mayor Lori Lightfoot, the companies broke the law by using "unfair and deceptive t

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  Super app

  A super app or super-app (also known as an everything app) is a mobile or web application that can provide multiple services including payment and instant messaging services, effectively becoming an all-encompassing, self-contained, commerce and communication online platform that embraces many aspects of personal and commercial life. Notable examples of super apps include Tencent's WeChat in China, Tata Neu in India, Grab in Southeast Asia and Max in Russia. For end users, a super app is an application that provides a set of core features while also giving access to independently developed miniapps. For app developers, a super app is an application integrated with the capabilities of platforms and ecosystems that allows third-parties to develop and publish miniapps. == History == The super app term was first used to describe WeChat when it combined the instant messaging service with the digital wallet function. Recognition of WeChat as a super app stems from its combination of messaging, payments, e-commerce, and much more within a single application, making it indispensable for many users. WeChat's establishment of the super app model has led companies like Meta to try to build similar applications outside of China. In India, Tata Group has announced that it is currently developing a super app named Tata Neu. Major Indian companies like Paytm, PhonePe, and ITC Maars also have apps in development that might constitute super apps. In Southeast Asia, Grab and Gojek lay claim to the super app classification despite lacking many of the features offered by WeChat. Accordingly, growth-stage companies like Shopee, Traveloka, and AirAsia have also expanded the range of services offered by their respective applications. == Notable examples == === Alipay === Alipay is a third-party mobile and online payment platform established in Hangzhou, China in February 2004 by Alibaba Group and its founder Jack Ma. It operates in association with Ant Group, an affiliate company of the Chinese Alibaba Group. === Gojek === Gojek is an Indonesian on-demand multiservice digital platform and fintech payment super app. Established in Jakarta in 2010, as a call center to connect consumers to courier delivery and two-wheeled ride-hailing services, it launched its mobile app in 2015 with four services: GoRide, GoSend, GoShop, and GoFood, which has since expanded to offer over 20 services. In 2021, it merged with another Indonesian unicorn, Tokopedia, forming the decacorn GoTo Gojek Tokopedia. === Grab === Grab is a Southeast Asian technology company headquartered in Singapore and Indonesia. Founded in 2012 as the MyTeksi app in Kuala Lumpur, Malaysia, it expanded the following year as GrabTaxi, before moving its headquarters to Singapore in 2014 and rebranding officially as Grab. In addition to ride-hailing and transportation services, the company's mobile app also offers food delivery and digital payment services. === Max === Max is a messenger from the Russian company VK, positioned as a super app. The application combines messaging, calls, and channels features with the integration of additional services: payments, miniapps, taxi ordering, deliveries, and other everyday services are available within a single interface. The goal is to unite communication and routine tasks in a unified ecosystem. === Tata Neu === Tata Neu is a multipurpose super app, developed in India by the Tata Group. It is the country's first super app. The app was launched to coincide with the start of a 2022 Indian Premier League cricket match. === WeChat === WeChat is a Chinese multipurpose instant messaging, social media and mobile payment app. First released in 2011, it became the world's largest standalone mobile app in 2018, with over 1 billion monthly active users. WeChat provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, the sharing of photographs and videos and location sharing. === X === X is an American social network, originally known as Twitter from its launch through 2023. Prior to his acquisition of the service, new owner Elon Musk stated that he planned for Twitter to become an "everything app" known as "X"; in 2023, the service added an AI chatbot known as "Grok" as well as integrated job search tools known as "X Hiring". In January 2025, X announced its intent to offer a digital wallet service in the future. Later in the year, X revamped its direct messaging system as "Chat". == Criticism == Although apps that fit the super app classification can offer users a wider variety of services in comparison to single-purpose alternatives, internet regulators in regions such as the US and Europe have become more concerned about the overall power of the technology industry and have become more critical of companies developing such apps. In China, WeChat and other local firms have been ordered to open up their platforms to rivals by local regulators. There are also reports that suggest it might be difficult to replicate WeChat's super app model. This stems partly from the peaking of smartphone penetration rates in many regions worldwide, which has led to overcrowded app stores and tighter restrictions on targeted advertising as regulators assert more control over the companies. From a technical viewpoint, single-purpose apps are comparatively faster, more responsive and easier to navigate than super apps, which helps improve the overall user experience. Super-apps are also likelier to store larger amounts of personal data to facilitate the delivery of their services, so users run a greater risk of becoming victims of severe data breaches. In 2020, this unfolded with Tokopedia, which had the data of 91 million of its users stolen and shared by crackers. It has also been noted that a user who loses access to their account or is banned from a super app generally loses access to multiple real-life services and digital applications; the Chinese government has used this approach to penalize people who shared the photos of the Sitong Bridge protest.

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  Site reliability engineering

  Site reliability engineering (SRE) is a discipline in the field of software engineering and IT infrastructure support that monitors and improves the availability and performance of deployed software systems and large software services (which are expected to deliver reliable response times across events such as new software deployments, hardware failures, and cybersecurity attacks). There is typically a focus on automation and an infrastructure as code methodology. SRE uses elements of software engineering, IT infrastructure, web development, and operations to assist with reliability. It is similar to DevOps as they both aim to improve the reliability and availability of deployed software systems. == History == Site Reliability Engineering originated at Google with Benjamin Treynor Sloss, who founded SRE team in 2003. The concept expanded within the software development industry, leading various companies to employ site reliability engineers. By March 2016, Google had more than 1,000 site reliability engineers on staff. Dedicated SRE teams are common at larger web development companies. In middle-sized and smaller companies, DevOps teams sometimes perform SRE, as well. Organizations that have adopted the concept include Airbnb, Dropbox, IBM, LinkedIn, Netflix, and Wikimedia. == Definition == Site reliability engineers (SREs) are responsible for a combination of system availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning. SREs often have backgrounds in software engineering, systems engineering, and/or system administration. The focuses of SRE include automation, system design, and improvements to system resilience. SRE is considered a specific implementation of DevOps; focusing specifically on building reliable systems, whereas DevOps covers a broader scope of operations. Despite having different focuses, some companies have rebranded their operations teams to SRE teams. == Principles and practices == Common definitions of the practices include (but are not limited to): Automation of repetitive tasks for cost-effectiveness. Defining reliability goals to prevent endless effort. Design of systems with a goal to reduce risks to availability, latency, and efficiency. Observability, the ability to ask arbitrary questions about a system without having to know ahead of time what to ask. Common definitions of the principles include (but are not limited to): Toil management, the implementation of the first principle outlined above. Defining and measuring reliability goals—SLIs, SLOs, and error budgets. Non-Abstract Large Scale Systems Design (NALSD) with a focus on reliability. Designing for and implementing observability. Defining, testing, and running an incident management process. Capacity planning. Change and release management, including CI/CD. Chaos engineering. == Deployment == SRE teams collaborate with other departments within organizations to guide the implementation of the mentioned principles. Below is an overview of common practices: === Kitchen Sink === Kitchen Sink refers to the expansive and often unbounded scope of services and workflows that SRE teams oversee. Unlike traditional roles with clearly defined boundaries, SREs are tasked with various responsibilities, including system performance optimization, incident management, and automation. This approach allows SREs to address multiple challenges, ensuring that systems run efficiently and evolve in response to changing demands and complexities. === Infrastructure === Infrastructure SRE teams focus on maintaining and improving the reliability of systems that support other teams' workflows. While they sometimes collaborate with platform engineering teams, their primary responsibility is ensuring up-time, performance, and efficiency. Platform teams, on the other hand, primarily develop the software and systems used across the organization. While reliability is a goal for both, platform teams prioritize creating and maintaining the tools and services used by internal stakeholders, whereas Infrastructure SRE teams are tasked with ensuring those systems run smoothly and meet reliability standards. === Tools === SRE teams utilize a variety of tools with the aim of measuring, maintaining, and enhancing system reliability. These tools play a role in monitoring performance, identifying issues, and facilitating proactive maintenance. For instance, Nagios Core is commonly employed for system monitoring and alerting, while Prometheus (software) is frequently used for collecting and querying metrics in cloud-native environments. === Product or Application === SRE teams dedicated to specific products or applications are common in large organizations. These teams are responsible for ensuring the reliability, scalability, and performance of key services. In larger companies, it's typical to have multiple SRE teams, each focusing on different products or applications, ensuring that each area receives specialized attention to meet performance and availability targets. === Embedded === In an embedded model, individual SREs or small SRE pairs are integrated within software engineering teams. These SREs collaborate with developers, applying core SRE principles—such as automation, monitoring, and incident response—directly to the software development lifecycle. This approach aims to enhance reliability, performance, and collaboration between SREs and developers. === Consulting === Consulting SRE teams specialize in advising organizations on the implementation of SRE principles and practices. Typically composed of seasoned SREs with a history across various implementations, these teams provide insights and guidance for specific organizational needs. When working directly with clients, these SREs are often referred to as 'Customer Reliability Engineers.' In large organizations that have adopted SRE, a hybrid model is common. This model includes various implementations, such as multiple Product/Application SRE teams dedicated to addressing the specific reliability needs of different products. An Infrastructure SRE team may collaborate with a Platform engineering group to achieve shared reliability goals for a unified platform that supports all products and applications. == Industry == Since 2014, the USENIX organization has hosted the annual SREcon conference, bringing together site reliability engineers from various industries. This conference is a platform for professionals to share knowledge, explore effective practices, and discuss trends in site reliability engineering.

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  Distribution management system

  A distribution management system (DMS) is a collection of applications designed to monitor and control the electric power distribution networks efficiently and reliably. It acts as a decision support system to assist the control room and field operating personnel with the monitoring and control of the electric distribution system. Improving the reliability and quality of service in terms of reducing power outages, minimizing outage time, maintaining acceptable frequency and voltage levels are the key deliverables of a DMS. Given the complexity of distribution grids, such systems may involve communication and coordination across multiple components. For example, the control of active loads may require a complex chain of communication through different components as described in US patent 11747849B2 In recent years, utilization of electrical energy increased exponentially and customer requirement and quality definitions of power were changed enormously. As electric energy became an essential part of daily life, its optimal usage and reliability became important. Real-time network view and dynamic decisions have become instrumental for optimizing resources and managing demands, leading to the need for distribution management systems in large-scale electrical networks. == Overview == Most distribution utilities have been comprehensively using IT solutions through their Outage Management System (OMS) that makes use of other systems like Customer Information System (CIS), Geographical Information System (GIS) and Interactive Voice Response System (IVRS). An outage management system has a network component/connectivity model of the distribution system. By combining the locations of outage calls from customers with knowledge of the locations of the protection devices (such as circuit breakers) on the network, a rule engine is used to predict the locations of outages. Based on this, restoration activities are charted out and the crew is dispatched for the same. In parallel with this, distribution utilities began to roll out Supervisory Control and Data Acquisition (SCADA) systems, initially only at their higher voltage substations. Over time, use of SCADA has progressively extended downwards to sites at lower voltage levels. DMSs access real-time data and provide all information on a single console at the control centre in an integrated manner. Their development varied across different geographic territories. In the US, for example, DMSs typically grew by taking Outage Management Systems to the next level, automating the complete sequences and providing an end to end, integrated view of the entire distribution spectrum. In the UK, by contrast, the much denser and more meshed network topologies, combined with stronger Health & Safety regulation, had led to early centralisation of high-voltage switching operations, initially using paper records and schematic diagrams printed onto large wallboards which were 'dressed' with magnetic symbols to show the current running states. There, DMSs grew initially from SCADA systems as these were expanded to allow these centralised control and safety management procedures to be managed electronically. These DMSs required even more detailed component/connectivity models and schematics than those needed by early OMSs as every possible isolation and earthing point on the networks had to be included. In territories such as the UK, therefore, the network component/connectivity models were usually developed in the DMS first, whereas in the USA these were generally built in the GIS. The typical data flow in a DMS has the SCADA system, the Information Storage & Retrieval (ISR) system, Communication (COM) Servers, Front-End Processors (FEPs) & Field Remote Terminal Units (FRTUs). == Why DMS? == Reduce the duration of outages Improve the speed and accuracy of outage predictions. Reduce crew patrol and drive times through improved outage locating. Improve the operational efficiency Determine the crew resources necessary to achieve restoration objectives. Effectively utilize resources between operating regions. Determine when best to schedule mutual aid crews. Increased customer satisfaction A DMS incorporates IVR and other mobile technologies, through which there is an improved outage communications for customer calls. Provide customers with more accurate estimated restoration times. Improve service reliability by tracking all customers affected by an outage, determining electrical configurations of every device on every feeder, and compiling details about each restoration process. == DMS Functions == In order to support proper decision making and O&M activities, DMS solutions should support the following functions: Network visualization & support tools Applications for Analytical & Remedial Action Utility Planning Tools System Protection Schemes The various sub functions of the same, carried out by the DMS are listed below:- === Network Connectivity Analysis (NCA) === Distribution network usually covers over a large area and catering power to different customers at different voltage levels. So locating required sources and loads on a larger GIS/Operator interface is often very difficult. Panning & zooming provided with normal SCADA system GUI does not cover the exact operational requirement. Network connectivity analysis is an operator specific functionality which helps the operator to identify or locate the preferred network or component very easily. NCA does the required analyses and provides display of the feed point of various network loads. Based on the status of all the switching devices such as circuit breaker (CB), Ring Main Unit (RMU) and/or isolators that affect the topology of the network modeled, the prevailing network topology is determined. The NCA further assists the operator to know operating state of the distribution network indicating radial mode, loops and parallels in the network. === Switching Schedule & Safety Management === In territories such as the UK a core function of a DMS has always been to support safe switching and work on the networks. Control engineers prepare switching schedules to isolate and make safe a section of network before work is carried out, and the DMS validates these schedules using its network model. Switching schedules can combine telecontrolled and manual (on-site) switching operations. When the required section has been made safe, the DMS allows a Permit To Work (PTW) document to be issued. After its cancellation when the work has been finished, the switching schedule then facilitates restoration of the normal running arrangements. Switching components can also be tagged to reflect any Operational Restrictions that are in force. The network component/connectivity model, and associated diagrams, must always be kept absolutely up to date. The switching schedule facility therefore also allows 'patches' to the network model to be applied to the live version at the appropriate stage(s) of the jobs. The term 'patch' is derived from the method previously used to maintain the wallboard diagrams. === State Estimation (SE) === The state estimator is an integral part of the overall monitoring and control systems for transmission networks. It is mainly aimed at providing a reliable estimate of the system voltages. This information from the state estimator flows to control centers and database servers across the network. The variables of interest are indicative of parameters like margins to operating limits, health of equipment and required operator action. State estimators allow the calculation of these variables of interest with high confidence despite the facts that the measurements may be corrupted by noise, or could be missing or inaccurate. Even though we may not be able to directly observe the state, it can be inferred from a scan of measurements which are assumed to be synchronized. The algorithms need to allow for the fact that presence of noise might skew the measurements. In a typical power system, the State is quasi-static. The time constants are sufficiently fast so that system dynamics decay away quickly (with respect to measurement frequency). The system appears to be progressing through a sequence of static states that are driven by various parameters like changes in load profile. The inputs of the state estimator can be given to various applications like Load Flow Analysis, Contingency Analysis, and other applications. === Load Flow Applications (LFA) === Load flow study is an important tool involving numerical analysis applied to a power system. The load flow study usually uses simplified notations like a single-line diagram and focuses on various forms of AC power rather than voltage and current. It analyzes the power systems in normal steady-state operation. The goal of a power flow study is to obtain complete voltage angle and magnitude information for each bus in a power system for specified load and generator real power and voltage conditions. Once this

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  BiP (software)

  BiP is a freeware instant messaging application developed by Lifecell Ventures Cooperatief U.A., a subsidiary of Turkcell incorporated in the Netherlands. It allows users to send text messages, voice messages and video calling, and it can be downloaded from the App Store, Google Play, and Huawei AppGallery. BiP has over 53 million users worldwide, and was first released in 2013. == Functions == BiP is a secure, and free communication platform. BiP allows making video and audio calls, allows sharing images, videos and location. BiP includes instant translations to 106 languages and exchange rates. President Erdoğan's Communications Office opposed WhatsApp's enforcement of its updated privacy policy and announced that Erdoğan left WhatsApp and opened an account in Telegram and BiP. The Turkish Ministry of National Defense has announced that it will move information groups to BiP for the same reason. == Others == Banglalink announced a BiP messenger partnership in Bangladesh The Communications Office of President Erdoğan opposed WhatsApp's enforcement of its updated privacy policy and announced that Erdoğan left WhatsApp and opened an account in Telegram and BiP. The Turkish Ministry of National Defense has announced that it will move information groups to BiP for the same reason. The CEO of BiP is Burak Akinci. The number of downloads of the app is 80 million globally.

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