AI Essay Bypass

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Vicuna LLM

Vicuna LLM is an omnibus large language model used in AI research. Its methodology is to enable the public at large to contrast and compare the accuracy of LLMs "in the wild" (an example of citizen science) and to vote on their output; a question-and-answer chat format is used. At the beginning of each round two LLM chatbots from a diverse pool of nine are presented randomly and anonymously, their identities only being revealed upon voting on their answers. The user has the option of either replaying ("regenerating") a round, or beginning an entirely fresh one with new LLMs. (The user also has the option of choosing which LLMs to do battle.) Based on Llama 2, it is an open source project, and it itself has become the subject of academic research in the burgeoning field. A non-commercial, public demo of the Vicuna-13b model is available to access using LMSYS.
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Colour banding

Colour banding is a subtle form of posterisation in digital images, caused by the colour of each pixel being rounded to the nearest of the digital colour levels. While posterisation is often done for artistic effect, colour banding is an undesired artefact. In 24-bit colour modes, 8 bits per channel is usually considered sufficient to render images in Rec. 709 or sRGB. However the eye can see the difference between the colour levels, especially when there is a sharp border between two large areas of adjacent colour levels. This will happen with gradual gradients (like sunsets, dawns or clear blue skies), and also when blurring an image a large amount. Colour banding is more noticeable with fewer bits per pixel (BPP) at 16–256 colours (4–8 BPP), where there are fewer shades with a larger difference between them. The appearance of colour banding is exaggerated by the Mach bands effect. Possible solutions include the introduction of dithering and increasing the number of bits per colour channel. Because the banding comes from limitations in the presentation of the image, blurring the image does not fix this unless the image BPP is higher than the original.
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MetaMask

MetaMask is a software cryptocurrency wallet developed by ConsenSys for interacting with the Ethereum blockchain and other EVM-compatible networks. It enables users to manage Ethereum accounts and connect to decentralized applications (dApps) via a browser extension or mobile app. As of early 2026, MetaMask reports over 100 million users worldwide. == Overview == MetaMask allows users to store and manage private keys, send and receive Ethereum-based cryptocurrencies and tokens (including ERC-20 and ERC-721 standards), broadcast transactions, and interact with dApps. dApps connect to the wallet via JavaScript interfaces, prompting users to approve signatures or transactions. The wallet features MetaMask Swaps, an in-app token swap aggregator sourcing liquidity from multiple decentralized exchanges (DEXs), with a service fee of 0.875%. In 2025, MetaMask introduced the MetaMask Rewards program (initially mobile-only), where users earn points for activities such as swaps, bridging, and referrals. Season 1 (October 2025 – January 2026) distributed over $30 million in Linea tokens and other perks to participants. == History == MetaMask launched in 2016 as open-source software under the MIT license. It initially supported browser extensions for Chrome and Firefox. Mobile versions were in closed beta from 2019 and publicly released for iOS and Android in September 2020. In August 2020, the license changed to a custom proprietary one. MetaMask Swaps launched on desktop in October 2020 and on mobile in March 2021. The Rewards program launched in late 2025 with Linea integration. == Criticism == MetaMask has faced criticism over privacy, including default analytics settings that share some user data (which can be disabled). Its reliance on Infura (acquired by ConsenSys in 2019) has raised concerns about centralization in Ethereum infrastructure. The wallet regularly issues warnings about phishing scams and fake airdrops impersonating MetaMask.
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TowIt

"TowIt" is a free, global, cross-platform mobile app, website, and Web API that allows civilians to report parking violations and dangerous driving in real-time. The mission is to remove the barriers required to make cities effectively fight and deter bad parking and dangerous driving habits. The company ultimately aims to better existing social controls in order to drive necessary behavioral change through increased education, real-time reporting, optimized enforcement, as well as the resulting reactivity. == User base and adoption == The application has users reporting vehicular infractions in upwards of 30 countries. The top reporting countries are: Portugal, Canada, United States of America and Australia. Users have adopted TowIt for a variety of reasons, usually central to their geographical location and the prominent offences in those specific areas. For instance, the majority of Portuguese reports are cars parked on sidewalks, footpaths and pedestrian crossings, Australian reports are largely focused on the abuse of disabled parking spaces, and in Toronto or San Francisco users generally capture cars parked in bicycle lanes. == Functions == === Data collection === TowIt gathers data on individual parking offences, the prominence of various offence types, as well as recurring offenders. This allows the company to identify trends and hotspots in order to take action against problem vehicles, as well as to help improve urban planning, traffic congestion and gridlock management. Individuals modify or improve an aspect of their behavior in response to their awareness of being observed, theoretically more so when demonstrating selfishness, egocentrism, narcissism and anti-social behavior. The company states that by becoming a user, one can "help TowIt relieve congestion, reduce collisions, open up economies, improve the environment and enhance the lives of urban residents and suburban commuters alike". The company has acknowledged that there are numerous legislative changes that would be required to integrate with governments at any level in many countries. A simple three-step process allows users to take a photo of an offending vehicle and subsequently verifying the offending vehicle's license plate information before submitting by tapping the TowIt (submit) button. Photographical evidence can only be captured with the camera from within the TowIt application. An Internet connection is required. The company has stated that this was purposefully done for quality control and report validation purposes. Users may only submit and view their own report history on either the iOS or Android applications. Globally submitted reports are displayed uncensored and in aggregate only on the Android application and the TowIt website. The "Global Feed" feature was removed from iOS (see iTunes Connect Acceptance Issues). TowIt's back-end automatically geotags the report and compares it to local parking by-law data, including by-law types, locations, times, side(s) of street, etc.- where available. Valid reports are posted to the global feed, to the TowIt website, and passed on to municipalities and police for enforcement (where connected). === Technologies used under license === TowIt currently utilizes the following software or software libraries under license: AngularJS, Apache Cordova, Apple iTunes Store EULA, Chart.js, Google Play Distribution Agreement, Ionic Framework, MongoDB, Moment.js, Python 2.7, Python Flask, and jQuery. == Company history == The TowIt application was conceived by Michael Duncan McArthur on December 5, 2014, as a response to Toronto Mayor John Tory's election mandate to "get this city moving". The application was announced via TowIt's official Twitter page on January 6, 2015. After the initial public announcement, Michael & Gregory were contacted by members of John Tory's staff on January 8, 2015, and invited to demo a prototype at Toronto City Hall on January 12, 2015. The two were also invited to meet with Toronto Councillor Norm Kelly, in his City Hall office, for a subsequent demo of the live Android application on January 28, 2015. A similar meeting and demo took place with members of the Traffic Services department of Toronto Police Service on February 2, 2015. Michael & Gregory teamed up with friends and Toronto-based developers Dae-Seon Moon, Jesse Malone, and Marcus Veres to complete the prototype in time to meet the city's imposed demo deadline and to launch the initial Android version of the application. TowIt officially launched on the Android platform on January 16, 2015. A subsequent iOS launch took place on March 19, 2015. === iTunes connect acceptance issues === The iOS version of the application was delayed for approximately two months, only after significant deliberation with Apple's iTunes Connect review board around (as then stated) rule: "14.1 - Any App that is defamatory, offensive, mean-spirited, or likely to place the targeted individual or group in harm's way will be rejected." The result was having to remove the "Global Feed" feature from the iOS platform, in which civilian users could view all recent reports from within the application. This feature still exists on the Android platform. === Business and legal === TowIt engaged Wildeboer Dellelce, one of Canada's leading business law and transactional corporate finance law firms, on January 17, 2015. The company filed for incorporation as "TowIt Solutions Inc." by both Michael & Gregory in the Canadian province of Ontario on January 22, 2015. TowIt continues to operate under a Freemium business model. The company is 100% bootstrapped and has received no outside investment to date. TowIt was accepted into the MaRS Discovery District's Venture Services program on March 4, 2015. === Lobbyist registration === After receiving initial press coverage in January and February 2015, an unknown entity reported Michael & Gregory's initial communications with city staff to the City of Toronto's Lobbyist Registrar. This complaint resulted in legal threats of fines received on February 10, 2015, for apparently and unknowingly breaking municipal lobbying by-laws. These fines (of up to $100,000) were eventually withdrawn after Michael & Gregory immediately provided all records of communication with city officials and registered as lobbyists in the City of Toronto on the subjects of By-law / Regulation, Parking, and Technology. Their registration was accepted by the Lobbyist Registrar on March 6, 2015. However, communication with Toronto city staff was reduced greatly as a result, which the company believes may have been the desired intent of the original complaint. === Outreach and activism === TowIt encourages its global user base to reach out to their local government representatives to promote the app at the users' own will. This tactic is used not only to demonstrate grassroots support, but also to avoid future lobbying issues. On June 2, 2015, the company officially partnered with Australian campaign "No Permit No Park" who advocate for the creation of inclusive communities. == Reception == The Best Planning Apps for 2016 by Planetizen, 5 Toronto apps you should be using by Indie88, 12 Best Apps Made In Canada by TechVibes.
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Cognitive computing

Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, among other technologies. == Definition == At present, there is no widely agreed upon definition for cognitive computing in either academia or industry. In general, the term cognitive computing has been used to refer to new hardware and/or software that mimics the functioning of the human brain (2004). In this sense, cognitive computing is a new type of computing with the goal of more accurate models of how the human brain/mind senses, reasons, and responds to stimulus. Cognitive computing applications link data analysis and adaptive page displays (AUI) to adjust content for a particular type of audience. As such, cognitive computing hardware and applications strive to be more affective and more influential by design. The term "cognitive system" also applies to any artificial construct able to perform a cognitive process where a cognitive process is the transformation of data, information, knowledge, or wisdom to a new level in the DIKW Pyramid. While many cognitive systems employ techniques having their origination in artificial intelligence research, cognitive systems, themselves, may not be artificially intelligent. For example, a neural network trained to recognize cancer on an MRI scan may achieve a higher success rate than a human doctor. This system is certainly a cognitive system but is not artificially intelligent. Cognitive systems may be engineered to feed on dynamic data in real-time, or near real-time, and may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). == Cognitive analytics == Cognitive computing-branded technology platforms typically specialize in the processing and analysis of large, unstructured datasets. == Applications == Education Even if cognitive computing can not take the place of teachers, it can still be a heavy driving force in the education of students. Cognitive computing being used in the classroom is applied by essentially having an assistant that is personalized for each individual student. This cognitive assistant can relieve the stress that teachers face while teaching students, while also enhancing the student's learning experience over all. Teachers may not be able to pay each and every student individual attention, this being the place that cognitive computers fill the gap. Some students may need a little more help with a particular subject. For many students, Human interaction between student and teacher can cause anxiety and can be uncomfortable. With the help of Cognitive Computer tutors, students will not have to face their uneasiness and can gain the confidence to learn and do well in the classroom. While a student is in class with their personalized assistant, this assistant can develop various techniques, like creating lesson plans, to tailor and aid the student and their needs. Healthcare Numerous tech companies are in the process of developing technology that involves cognitive computing that can be used in the medical field. The ability to classify and identify is one of the main goals of these cognitive devices. This trait can be very helpful in the study of identifying carcinogens. This cognitive system that can detect would be able to assist the examiner in interpreting countless numbers of documents in a lesser amount of time than if they did not use Cognitive Computer technology. This technology can also evaluate information about the patient, looking through every medical record in depth, searching for indications that can be the source of their problems. Commerce Together with Artificial Intelligence, it has been used in warehouse management systems to collect, store, organize and analyze all related supplier data. All these aims at improving efficiency, enabling faster decision-making, monitoring inventory and fraud detection Human Cognitive Augmentation In situations where humans are using or working collaboratively with cognitive systems, called a human/cog ensemble, results achieved by the ensemble are superior to results obtainable by the human working alone. Therefore, the human is cognitively augmented. In cases where the human/cog ensemble achieves results at, or superior to, the level of a human expert then the ensemble has achieved synthetic expertise. In a human/cog ensemble, the "cog" is a cognitive system employing virtually any kind of cognitive computing technology. Other use cases Speech recognition Sentiment analysis Face detection Risk assessment Fraud detection Behavioral recommendations == Industry work == Cognitive computing in conjunction with big data and algorithms that comprehend customer needs, can be a major advantage in economic decision making. The powers of cognitive computing and artificial intelligence hold the potential to affect almost every task that humans are capable of performing. This can negatively affect employment for humans, as there would be no such need for human labor anymore. It would also increase the inequality of wealth; the people at the head of the cognitive computing industry would grow significantly richer, while workers without ongoing, reliable employment would become less well off. The more industries start to use cognitive computing, the more difficult it will be for humans to compete. Increased use of the technology will also increase the amount of work that AI-driven robots and machines can perform. The influence of competitive individuals in conjunction with artificial intelligence/cognitive computing has the potential to change the course of humankind.
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Online service provider

An online service provider (OSP) can, for example, be an Internet service provider, an email provider, a news provider (press), an entertainment provider (music, movies), a search engine, an e-commerce site, an online banking site, a health site, an official government site, social media, a wiki, or a Usenet newsgroup. In its original more limited definition, it referred only to a commercial computer communication service in which paid members could dial via a computer modem the service's private computer network and access various services and information resources such as bulletin board systems, downloadable files and programs, news articles, chat rooms, and electronic mail services. The term "online service" was also used in references to these dial-up services. The traditional dial-up online service differed from the modern Internet service provider in that they provided a large degree of content that was only accessible by those who subscribed to the online service, while ISP mostly serves to provide access to the Internet and generally provides little if any exclusive content of its own. In the U.S., the Online Copyright Infringement Liability Limitation Act (OCILLA) portion of the U.S. Digital Millennium Copyright Act has expanded the legal definition of online service in two different ways for different portions of the law. It states in section 512(k)(1): (A) As used in subsection (a), the term "service provider" means an entity offering the transmission, routing, or providing of connections for digital online communications, between or among points specified by a user, of material of the user's choosing, without modification to the content of the material as sent or received. (B) As used in this section, other than subsection (a), the term "service provider" means a provider of online services or network access, or the operator of facilities therefore, and includes an entity described in subparagraph (A). These broad definitions make it possible for numerous web businesses to benefit from the OCILLA. == History == The first commercial online services went live in 1969. CompuServe (owned in the 1980s and 1990s by H&R Block) and The Source (for a time owned by The Reader's Digest) are considered the first major online services created to serve the market of personal computer users. Utilizing text-based interfaces and menus, these services allowed anyone with a modem and communications software to use email, chat, news, financial and stock information, bulletin boards, special interest groups (SIGs), forums and general information. Subscribers could exchange email only with other subscribers of the same service. (For a time a service called DASnet carried mail among several online services, and CompuServe, MCI Mail, and other services experimented with X.400 protocols to exchange email until the Internet rendered these outmoded.) Other text-based online services followed such as Delphi, GEnie and MCI Mail. The 1980s also saw the rise of independent Computer Bulletin Boards, or BBSes. (Online services are not BBSes. An online service may contain an electronic bulletin board, but the term "BBS" is reserved for independent dialup, microcomputer-based services that are usually single-user systems.) The commercial services used pre-existing packet-switched (X.25) data communications networks, or the services' own networks (as with CompuServe). In either case, users dialed into local access points and were connected to remote computer centers where information and services were located. As with telephone service, subscribers paid by the minute, with separate day-time and evening/weekend rates. As the use of computers that supported color and graphics, such the Atari 8-bit computers, Commodore 64, TI-99/4A, Apple II, and early IBM PC compatibles, increased, online services gradually developed framed or partially graphical information displays. Early services such as CompuServe added increasingly sophisticated graphics-based front end software to present their information, though they continued to offer text-based access for those who needed or preferred it. In 1985 Viewtron, which began as a Videotex service requiring a dedicated terminal, introduced software allowing home computer owners access. Beginning in the mid-1980s graphics based online services such as PlayNET, Prodigy, and Quantum Link (aka Q-Link) were developed. Quantum Link, which was based on Commodore-only Playnet software, later developed AppleLink Personal Edition, PC-Link (based on Tandy's DeskMate), and Promenade (for IBM), all of which (including Q-Link) were later combined as America Online. These online services presaged the web browser that would change global online life 10 years later. Before Quantum Link, Apple computer had developed its own service, called AppleLink, which was mostly a support network targeted at Apple dealers and developers. Later, Apple offered the short-lived eWorld, targeted at Mac consumers and based on the Mac version of the America Online software. Beginning in 1992, the Internet, which had previously been limited to government, academic, and corporate research settings, was opened to commercial entities. The first online service to offer Internet access was DELPHI, which had developed TCP/IP access much earlier, in connection with an environmental group that rated Internet access. The explosion of popularity of the World Wide Web in 1994 accelerated the development of the Internet as an information and communication resource for consumers and businesses. The sudden availability of low- to no-cost email and appearance of free independent web sites broke the business model that had supported the rise of the early online service industry. CompuServe, BIX, AOL, DELPHI, and Prodigy gradually added access to Internet e-mail, Usenet newsgroups, ftp, and to web sites. At the same time, they moved from usage-based billing to monthly subscriptions. Similarly, companies that paid to have AOL host their information or early online stores began to develop their own web sites, putting further stress on the economics of the online industry. Only the largest services like AOL (which later acquired CompuServe, just as CompuServe acquired The Source) were able to make the transition to the Internet-centric world. A new class of online service provider arose to provide access to the Internet, the internet service provider or ISP. Internet-only service providers like UUNET, The Pipeline, Panix, Netcom, the World, EarthLink, and MindSpring provided no content of their own, concentrating their efforts on making it easy for nontechnical users to install the various software required to "get online" before consumer operating systems came internet-enabled out of the box. In contrast to the online services' multitiered per-minute or per-hour rates, many ISPs offered flat-fee, unlimited access plans. Independent companies sprang up to offer access and packages to compete with the big networks (eg, the-wire.com, 1994 in Toronto and bway.net 1995 in New York). These providers first offered access through telephone and modem, just as did the early online services providers. By the early 2000s, these independent ISPs had largely been supplanted by high speed and broadband access through cable and phone companies, as well as wireless access. The importance of the online services industry was vital in "paving the road" for the information superhighway. When Mosaic and Netscape were released in 1994, they had a ready audience of more than 10 million people who were able to download their first web browser through an online service. Though ISPs quickly began offering software packages with setup to their customers, this brief period gave many users their first online experience. Two online services in particular, Prodigy and AOL, are often confused with the Internet, or the origins of the Internet. Prodigy's Chief Technical Officer said in 1999: "Eleven years ago, the Internet was just an intangible dream that Prodigy brought to life. Now it is a force to be reckoned with." Despite that statement, neither service provided the back bone for the Internet, nor did either start the Internet. == Online service interfaces == The first online service used a simple text-based interface in which content was largely text only and users made choices via a command prompt. This allowed just about any computer with a modem and terminal communications program the ability to access these text-based online services. CompuServe would later offer, with the advent of the Apple Macintosh and Microsoft Windows-based PCs, a GUI interface program for their service. This provided a very rudimentary GUI interface. CompuServe continued to offer text-only access for those needing it. Online services like Prodigy and AOL developed their online service around a GUI and thus unlike CompuServe's early GUI-based software, these online services provided a more robust GUI interface. Early GUI-base
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Nagarik App

Nagarik App (translation: Citizen App) is a mobile application launched by the Government of Nepal to provide government-related services in a single online platform. The app was developed to facilitate an easier, systematic, and simplified delivery of government services to Nepali citizens digitally. The app was launched to play a pivotal role in revolutionizing the way citizens interact with the government. It offers government services through a single unified platform, minimizing the need for citizens to navigate multiple channels or physical offices for their diverse needs of government services. The services are added gradually according to the needs and services required. The government aims to reduce the physical queues and the need to be physically present to get services from the different government offices. One can get services online round-the-clock even during holidays. As of now, 25 services are included in the app, ranging from Police Clearance Report to Voters Card. The app contains and provides a vast range of government services. The app was launched on the occasion of the fourth National Information and Communication Technology Day, 2021 (2078 BS). The event marked a significant milestone in Nepal’s digital transformation journey. It aims to reduce all the bureaucratic hurdles that the citizens have been facing and make government services more efficient and convenient. In Oct 20, 2024, a E-Chalan was introduced for managing traffic violations in initially piloting in Kathmandu Valley. The Kathmandu Valley Traffic Police Office announced that physical licenses would no longer be confiscated for traffic rule violations. Instead, a "Digital Chit (E-Chalan)" system was implemented, allowing drivers to pay fines electronically. Integrated with the NagarikApp, the system enables police to access drivers' licenses, record violations, and update details directly in the app. == Features and Services == Inland Revenue Department (Nepal) PAN Registration Election Commission (Nepal) Voter Card Pre-Registration and Details Nepal Police Online Clearance Report Traffic Violations and Fine Payment Nepal Passport, Driving License, National Identity Card (NID), Citizenship, and Voter ID link details My Municipality (Includes contact info of the representatives, services such as ambulance, nearby police, and budget programs and plans) The Government Press ID card PF/PAN/SST/CIT statements can be viewed Nagarik Pahichan Dwar (Online bank accounts can be opened and KYC can be verified for selected banks using the QR) == Awards and honors == Each year, World Summit Award honors outstanding digital applications and solutions across various categories. The winners of the World Summit Award represent the pinnacle of innovation in their respective categories. Nagarik App was selected among 180 participants and won the World Summit Award of 2022 in Government and Citizen Engagement category. == Latest Statistics & Usage Trends (2082 BS / 2025 AD) == As of August 2025, over 1.5 million Nepali citizens have registered and actively use the Nagarik App, according to the National Information Technology Center (NITC). The majority of daily logins come from: Kathmandu Valley – 37% of total users Province 1 (Koshi) – 19% of total users Bagmati Province – 15% of total users On average, 45,000+ transactions (service requests, document verifications, and payments) are processed through the app each day. The most-used services include: PAN Card Registration – 28% of total requests Police Clearance Report – 22% Driving License Linking & E-Chalan Payment – 18% Vehicle Tax Payment – 14% Source: Internal report from NITC, July 2025 == Step-by-Step: How to Link Your Driving License with Nagarik App == Update the App – Install the latest version from Play Store or App Store. Login or Register – Ensure your SIM is registered in your own name. Go to “Transport Services” in the menu. Select “Driving License” – Enter your license number and date of birth. Verify via OTP – Sent to your registered mobile number. Confirmation – Your digital license will appear inside the app. This guide is continuously updated to reflect the latest rules from the Kathmandu Valley Traffic Police Office and changes in NITC’s backend system. For in-depth details, step-by-step tutorials, and the most recent Nagarik App updates, visit the full article on The Bipin Blog.
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Event store

An event store is a type of database optimized for storage of events. Conceptually, an event store records only the events affecting an entity, dossier, or policy, and the state of the entity at any point in its history can be reconstructed by replaying its contributing events in sequential order. Events (and their corresponding data) are the only "real" facts that should be stored in the database. All other objects can be derived from these events, meaning they are instantiated in memory by runtime code as needed (e.g. for showing in a user interface). In theory, any object that aggregates over recorded event data is not stored in the database. Instead these objects are built 'on the fly', by traversing the event history. When the aggregated object instance is no longer needed, it can simply be discarded (released from memory). == Example with insurance policies == For example, the event store concept of a database can be applied to insurance policies or pension dossiers. In these policies or dossiers the instantiation of each object that make up the dossier or policy (the person, partner(s), employments, etc.) can be derived and can be instantiated in memory based on the real world events. == Double timeline == A crucial part of an event store database is that each event has a double timeline: This enables event stores to correct errors of events that have been entered into the event store database before. The two dates are: Valid date is the date at which the event has become valid. Transaction date is the date at which the event is entered into the database. == Error correction == Another crucial part of an event store database is that events that are stored are not allowed to be changed. Once stored, also erroneous events are not changed anymore. The only way to change (or better: correct) these events is to instantiate a new event with the new values and using the double timeline. A correcting event would have the new values of the original event, with an event data of that corrected event, but a different transaction date. This mechanism ensures reproducibility at each moment in the time, even in the time period before the correction has taken place. It also allows to reproduce situations based on erroneous events (if required). == Advantages and disadvantages == One advantage of the event store concept is that handling the effects of back dated events (events that take effect before previous events and that may even invalidate them) is much easier. An event store will simplify the code in that rolling back erroneous situations and rolling up the new, correct situations is not needed anymore. Disadvantage may be that the code needs to re-instantiate all objects in memory based on the events each time a service call is received for a specific dossier or policy. == Compared to regular databases == In regular databases, handling backdated events to correct previous, erroneous events can be painful as it often results in rolling back all previous, erroneous transactions and objects and rolling up the new, correct transactions and objects. In an event store, only the new event (and its corresponding facts) are stored. The code will then redetermine the transactions and objects based on the new facts in memory.
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Statistical learning theory

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as computer vision, speech recognition, and bioinformatics. == Introduction == The goals of learning are understanding and prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood. Supervised learning involves learning from a training set of data. Every point in the training is an input–output pair, where the input maps to an output. The learning problem consists of inferring the function that maps between the input and the output, such that the learned function can be used to predict the output from future input. Depending on the type of output, supervised learning problems are either problems of regression or problems of classification. If the output takes a continuous range of values, it is a regression problem. Using Ohm's law as an example, a regression could be performed with voltage as input and current as an output. The regression would find the functional relationship between voltage and current to be R {\displaystyle R} , such that V = I R {\displaystyle V=IR} Classification problems are those for which the output will be an element from a discrete set of labels. Classification is very common for machine learning applications. In facial recognition, for instance, a picture of a person's face would be the input, and the output label would be that person's name. The input would be represented by a large multidimensional vector whose elements represent pixels in the picture. After learning a function based on the training set data, that function is validated on a test set of data, data that did not appear in the training set. == Formal description == Take X {\displaystyle X} to be the vector space of all possible inputs, and Y {\displaystyle Y} to be the vector space of all possible outputs. Statistical learning theory takes the perspective that there is some unknown probability distribution over the product space Z = X × Y {\displaystyle Z=X\times Y} , i.e. there exists some unknown p ( z ) = p ( x , y ) {\displaystyle p(z)=p(\mathbf {x} ,y)} . The training set is made up of n {\displaystyle n} samples from this probability distribution, and is notated S = { ( x 1 , y 1 ) , … , ( x n , y n ) } = { z 1 , … , z n } {\displaystyle S=\{(\mathbf {x} _{1},y_{1}),\dots ,(\mathbf {x} _{n},y_{n})\}=\{\mathbf {z} _{1},\dots ,\mathbf {z} _{n}\}} Every x i {\displaystyle \mathbf {x} _{i}} is an input vector from the training data, and y i {\displaystyle y_{i}} is the output that corresponds to it. In this formalism, the inference problem consists of finding a function f : X → Y {\displaystyle f:X\to Y} such that f ( x ) ∼ y {\displaystyle f(\mathbf {x} )\sim y} . Let H {\displaystyle {\mathcal {H}}} be a space of functions f : X → Y {\displaystyle f:X\to Y} called the hypothesis space. The hypothesis space is the space of functions the algorithm will search through. Let V ( f ( x ) , y ) {\displaystyle V(f(\mathbf {x} ),y)} be the loss function, a metric for the difference between the predicted value f ( x ) {\displaystyle f(\mathbf {x} )} and the actual value y {\displaystyle y} . The expected risk is defined to be I [ f ] = ∫ X × Y V ( f ( x ) , y ) p ( x , y ) d x d y {\displaystyle I[f]=\int _{X\times Y}V(f(\mathbf {x} ),y)\,p(\mathbf {x} ,y)\,d\mathbf {x} \,dy} The target function, the best possible function f {\displaystyle f} that can be chosen, is given by the f {\displaystyle f} that satisfies f = argmin h ∈ H ⁡ I [ h ] {\displaystyle f=\mathop {\operatorname {argmin} } _{h\in {\mathcal {H}}}I[h]} Because the probability distribution p ( x , y ) {\displaystyle p(\mathbf {x} ,y)} is unknown, a proxy measure for the expected risk must be used. This measure is based on the training set, a sample from this unknown probability distribution. It is called the empirical risk I S [ f ] = 1 n ∑ i = 1 n V ( f ( x i ) , y i ) {\displaystyle I_{S}[f]={\frac {1}{n}}\sum _{i=1}^{n}V(f(\mathbf {x} _{i}),y_{i})} A learning algorithm that chooses the function f S {\displaystyle f_{S}} that minimizes the empirical risk is called empirical risk minimization. == Loss functions == The choice of loss function is a determining factor on the function f S {\displaystyle f_{S}} that will be chosen by the learning algorithm. The loss function also affects the convergence rate for an algorithm. It is important for the loss function to be convex. Different loss functions are used depending on whether the problem is one of regression or one of classification. === Regression === The most common loss function for regression is the square loss function (also known as the L2-norm). This familiar loss function is used in Ordinary Least Squares regression. The form is: V ( f ( x ) , y ) = ( y − f ( x ) ) 2 {\displaystyle V(f(\mathbf {x} ),y)=(y-f(\mathbf {x} ))^{2}} The absolute value loss (also known as the L1-norm) is also sometimes used: V ( f ( x ) , y ) = | y − f ( x ) | {\displaystyle V(f(\mathbf {x} ),y)=|y-f(\mathbf {x} )|} === Classification === In some sense the 0-1 indicator function is the most natural loss function for classification. It takes the value 0 if the predicted output is the same as the actual output, and it takes the value 1 if the predicted output is different from the actual output. For binary classification with Y = { − 1 , 1 } {\displaystyle Y=\{-1,1\}} , this is: V ( f ( x ) , y ) = θ ( − y f ( x ) ) {\displaystyle V(f(\mathbf {x} ),y)=\theta (-yf(\mathbf {x} ))} where θ {\displaystyle \theta } is the Heaviside step function. == Regularization == In machine learning problems, a major problem that arises is that of overfitting. Because learning is a prediction problem, the goal is not to find a function that most closely fits the (previously observed) data, but to find one that will most accurately predict output from future input. Empirical risk minimization runs this risk of overfitting: finding a function that matches the data exactly but does not predict future output well. Overfitting is symptomatic of unstable solutions; a small perturbation in the training set data would cause a large variation in the learned function. It can be shown that if the stability for the solution can be guaranteed, generalization and consistency are guaranteed as well. Regularization can solve the overfitting problem and give the problem stability. Regularization can be accomplished by restricting the hypothesis space H {\displaystyle {\mathcal {H}}} . A common example would be restricting H {\displaystyle {\mathcal {H}}} to linear functions: this can be seen as a reduction to the standard problem of linear regression. H {\displaystyle {\mathcal {H}}} could also be restricted to polynomial of degree p {\displaystyle p} , exponentials, or bounded functions on L1. Restriction of the hypothesis space avoids overfitting because the form of the potential functions are limited, and so does not allow for the choice of a function that gives empirical risk arbitrarily close to zero. One example of regularization is Tikhonov regularization. This consists of minimizing 1 n ∑ i = 1 n V ( f ( x i ) , y i ) + γ ‖ f ‖ H 2 {\displaystyle {\frac {1}{n}}\sum _{i=1}^{n}V(f(\mathbf {x} _{i}),y_{i})+\gamma \left\|f\right\|_{\mathcal {H}}^{2}} where γ {\displaystyle \gamma } is a fixed and positive parameter, the regularization parameter. Tikhonov regularization ensures existence, uniqueness, and stability of the solution. == Bounding empirical risk == Consider a binary classifier f : X → { 0 , 1 } {\displaystyle f:{\mathcal {X}}\to \{0,1\}} . We can apply Hoeffding's inequality to bound the probability that the empirical risk deviates from the true risk to be a Sub-Gaussian distribution. P ( | R ^ ( f ) − R ( f ) | ≥ ϵ ) ≤ 2 e − 2 n ϵ 2 {\displaystyle \mathbb {P} (|{\hat {R}}(f)-R(f)|\geq \epsilon )\leq 2e^{-2n\epsilon ^{2}}} But generally, when we do empirical risk minimization, we are not given a classifier; we must choose it. Therefore, a more useful result is to bound the probability of the supremum of the difference over the whole class. P ( sup f ∈ F | R ^ ( f ) − R ( f ) | ≥ ϵ ) ≤ 2 S ( F , n ) e − n ϵ 2 / 8 ≈ n d e − n ϵ 2 / 8 {\displaystyle \mathbb {P} {\bigg (}\sup _{f\in {\mathcal {F}}}|{\hat {R}}(f)-R(f)|\geq \epsilon {\bigg )}\leq 2S({\mathcal {F}},n)e^{-n\epsilon ^{2}/8}\approx n^{d}e^{-n\epsilon ^{2}/8}} where S ( F , n ) {\displaystyle S({\mathcal {F}},n)} is the shattering number and n {\displaystyle n} is the number of samples in your dataset. The exponential term comes from Hoeffding but there is an extra cost of taking the supremum over the whole cla
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CrocBITE

CrocBITE (currently CrocAttack) was an online database of wild crocodilian attacks reported on humans in the world. The non-profit online research tool helped to scientifically analyze crocodilian behavior via complex models. Users were encouraged to feed information in a crowdsourcing manner. This website excludes captive crocodilian attacks, as well as non-fatal bites on professional handlers, rangers, staff, or researchers, and crocodilian attacks on pets and livestock, because its primary goal is to analyze natural human-crocodilian conflict in the wild for conservation and management purposes, and that these incidents do are not considered indicative of natural species behavior or typical human-wildlife conflict, as well as not providing enough useful data and helping researchers understand wild population behavior or typical human-wildlife conflict dynamics and helps create safety strategies for people living or working near wild crocodilians, rather than tracking workplace accidents in zoos or farms. While fatal incidents involving handlers are sometimes included on the website, typical captive incidents (such as handlers being bitten by them in zoos) are excluded because they are considered manageable professional risks rather than general public safety threats. == About == The online database was established in 2013 (2013) by Dr Adam Britton, a researcher at Charles Darwin University, his student Brandon Sideleau and Erin Britton. It was a compilation of government records, individual reports, registered contributors and historical data. Dr Simon Pooley, Junior Research fellow, Imperial College London joined hands to further the studies. The collaboration culminated when Dr Pooley met Dr Britton at the IUCN Crocodile Specialist Group, in Louisiana in 2014. The program received funds from Economic and Social Research Council, United Kingdom to the tune of A$30,000 and unspecified resourced plus amount from Big Gecko Crocodilian Research, Crocodillian.com and Charles Darwin University. The research yielded pertinent observations that provide inside into crocodile attacks. It was observed that most attacks on humans occur from bites of Saltwater crocodile as against the popular understanding of Nile crocodiles taking the top spot. This is not, however, believed to be the actual case, as most attacks by the Nile crocodile are believed to go unreported or only reported on a local level. The broad category of Nile crocodile attacks were segmented into West African crocodile and Crocodylus niloticus (the Nile Crocodile) species to get a clear understanding of their respective attack zones. The objective was that the information would be used by communities and conservation managers to help inform and educate people about how to keep safe. The information was vital for Australia and Africa where such attacks are more likely than in other parts of the world. This was the only database of its kind with such comprehensive collection of information made available online. The database is no longer online, and its founder Adam Britton is in custody having pleaded guilty to charges of bestiality on September 25, 2023. It has been rebranded and renamed CrocAttack, and serves as a updated database focusing on human-crocodilian conflict and records over 8,500 incidents from the past decades.
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Once (dating platform)

Once is an online dating platform founded in 2015. The platform offers users one selected match per day for more meaningful connections. == History == Once was established in 2015, the founders included dating industry entrepreneur Jean Meyer, who became a CEO of the company, as well as Guillaume Sempe and Guilhem Duche. It focused on providing a single daily match to its users. On its early stages Once secured a $3.5 million seed round from Partech Ventures and some private investors. The same year, it opened offices in Paris, and London. By 2016, it reached 1 million users. In 2020, the company was acquired by Dating Group for $18 million. Following the acquisition, Once underwent rebranding. Alexandra Beaumont took over leadership of the brand in 2021, driving growth, rebranding, and innovation. == Overview == Once provides an online dating service with a focus on thoughtful connections. Users receive one selected match per day, which encourages meaningful interactions. The platform operates primarily in the United States, the United Kingdom, Canada, France, and Spain. The platform is supported by Android, iOS, and Apple Watch OS.
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Cyber attribution

In the area of computer security, cyber attribution is an attribution of cybercrime, i.e., finding who perpetrated a cyberattack. Uncovering a perpetrator may give insights into various security issues, such as infiltration methods, communication channels, etc., and may help in enacting specific countermeasures. Cyber attribution is a costly endeavor requiring considerable resources and expertise in cyber forensic analysis. For governments and other major players dealing with cybercrime would require not only technical solutions, but legal and political ones as well, and for the latter ones cyber attribution is crucial. Attributing a cyberattack is difficult, and of limited interest to companies that are targeted by cyberattacks. In contrast, secret services often have a compelling interest in finding out whether a state is behind the attack. A further challenge in attribution of cyberattacks is the possibility of a false flag attack, where the actual perpetrator makes it appear that someone else caused the attack. Every stage of the attack may leave artifacts, such as entries in log files, that can be used to help determine the attacker's goals and identity. In the aftermath of an attack, investigators often begin by saving as many artifacts as they can find, and then try to determine the attacker.
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Wadhwani Institute for Artificial Intelligence

Wadhwani AI, based in Mumbai, Maharashtra, is an independent, non-profit institute. Founded in 2018, it is dedicated to developing Artificial intelligence solutions for social good. Their mission is to build AI-based innovations and solutions for underserved communities in developing countries, for a wide range of domains including agriculture, education, financial inclusion, healthcare, and infrastructure. == History and funding == The institute was founded with a $30 million philanthropic effort by the Wadhwani brothers, Romesh Wadhwani and Sunil Wadhwani. The institute was inaugurated and dedicated to the nation by Narendra Modi, the 14th Prime Minister of India. In 2019, the institute received a $2 million grant from Google.org to create technologies to help reduce crop losses in cotton farming, through integrated pest management. The United States Agency for International Development awarded $2 million to the institute in 2020 to develop tools, using mathematical modeling techniques and digital technologies such as artificial intelligence and machine learning, to forecast COVID-19 disease patterns, estimate resources needed, and plan interventions. == Collaboration == With assistance from Google, the Ministry of Agriculture and Farmers' Welfare and the Wadhwani AI developed Krishi 24/7, the first AI-powered automated agricultural news monitoring and analysis tool. Through better decision-making, Krishi 24/7 will support the identification of valuable news, provide timely notifications, and respond quickly to safeguard farmers' interests and advance sustainable agricultural growth. The application converts news articles into English after scanning them in several languages. It ensures that the ministry is informed in a timely manner about pertinent occurrences that are published online by extracting key information from news items, including the headline, crop name, event type, date, location, severity, summary, and source link. The National Center for Disease Control has effectively implemented a comparable automated surveillance and analysis tool for disease outbreaks.
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Snapshot isolation

In databases, and transaction processing (transaction management), snapshot isolation is a guarantee that all reads made in a transaction will see a consistent snapshot of the database (in practice it reads the last committed values that existed at the time it started), and the transaction itself will successfully commit only if no updates it has made conflict with any concurrent updates made since that snapshot. Snapshot isolation has been adopted by several major database management systems, such as InterBase, Firebird, Oracle, MySQL, PostgreSQL, SQL Anywhere, MongoDB and Microsoft SQL Server (2005 and later). The main reason for its adoption is that it allows better performance than serializability, yet still avoids most of the concurrency anomalies that serializability avoids (but not all). In practice snapshot isolation is implemented within multiversion concurrency control (MVCC), where generational values of each data item (versions) are maintained: MVCC is a common way to increase concurrency and performance by generating a new version of a database object each time the object is written, and allowing transactions' read operations of several last relevant versions (of each object). Snapshot isolation has been used to criticize the ANSI SQL-92 standard's definition of isolation levels, as it exhibits none of the "anomalies" that the SQL standard prohibited, yet is not serializable (the anomaly-free isolation level defined by ANSI). In spite of its distinction from serializability, snapshot isolation is sometimes referred to as serializable by Oracle. == Definition == A transaction executing under snapshot isolation appears to operate on a personal snapshot of the database, taken at the start of the transaction. When the transaction concludes, it will successfully commit only if the values updated by the transaction have not been changed externally since the snapshot was taken. Such a write–write conflict will cause the transaction to abort. In a write skew anomaly, two transactions (T1 and T2) concurrently read an overlapping data set (e.g. values V1 and V2), concurrently make disjoint updates (e.g. T1 updates V1, T2 updates V2), and finally concurrently commit, neither having seen the update performed by the other. Were the system serializable, such an anomaly would be impossible, as either T1 or T2 would have to occur "first", and be visible to the other. In contrast, snapshot isolation permits write skew anomalies. As a concrete example, imagine V1 and V2 are two balances held by a single person, Phil. The bank will allow either V1 or V2 to run a deficit, provided the total held in both is never negative (i.e. V1 + V2 ≥ 0). Both balances are currently $100. Phil initiates two transactions concurrently, T1 withdrawing $200 from V1, and T2 withdrawing $200 from V2. If the database guaranteed serializable transactions, the simplest way of coding T1 is to deduct $200 from V1, and then verify that V1 + V2 ≥ 0 still holds, aborting if not. T2 similarly deducts $200 from V2 and then verifies V1 + V2 ≥ 0. Since the transactions must serialize, either T1 happens first, leaving V1 = −$100, V2 = $100, and preventing T2 from succeeding (since V1 + (V2 − $200) is now −$200), or T2 happens first and similarly prevents T1 from committing. If the database is under snapshot isolation(MVCC), however, T1 and T2 operate on private snapshots of the database: each deducts $200 from an account, and then verifies that the new total is zero, using the other account value that held when the snapshot was taken. Since neither update conflicts, both commit successfully, leaving V1 = V2 = −$100, and V1 + V2 = −$200. Some systems built using multiversion concurrency control (MVCC) may support (only) snapshot isolation to allow transactions to proceed without worrying about concurrent operations, and more importantly without needing to re-verify all read operations when the transaction finally commits. This is convenient because MVCC maintains a series of recent history consistent states. The only information that must be stored during the transaction is a list of updates made, which can be scanned for conflicts fairly easily before being committed. However, MVCC systems (such as MarkLogic) will use locks to serialize writes together with MVCC to obtain some of the performance gains and still support the stronger "serializability" level of isolation. == Workarounds == Potential inconsistency problems arising from write skew anomalies can be fixed by adding (otherwise unnecessary) updates to the transactions in order to enforce the serializability property. Materialize the conflict Add a special conflict table, which both transactions update in order to create a direct write–write conflict. Promotion Have one transaction "update" a read-only location (replacing a value with the same value) in order to create a direct write–write conflict (or use an equivalent promotion, e.g. Oracle's SELECT FOR UPDATE). In the example above, we can materialize the conflict by adding a new table which makes the hidden constraint explicit, mapping each person to their total balance. Phil would start off with a total balance of $200, and each transaction would attempt to subtract $200 from this, creating a write–write conflict that would prevent the two from succeeding concurrently. However, this approach violates the normal form. Alternatively, we can promote one of the transaction's reads to a write. For instance, T2 could set V1 = V1, creating an artificial write–write conflict with T1 and, again, preventing the two from succeeding concurrently. This solution may not always be possible. In general, therefore, snapshot isolation puts some of the problem of maintaining non-trivial constraints onto the user, who may not appreciate either the potential pitfalls or the possible solutions. The upside to this transfer is better performance. == Terminology == Snapshot isolation is called "serializable" mode in Oracle and PostgreSQL versions prior to 9.1, which may cause confusion with the "real serializability" mode. There are arguments both for and against this decision; what is clear is that users must be aware of the distinction to avoid possible undesired anomalous behavior in their database system logic. == History == Snapshot isolation arose from work on multiversion concurrency control databases, where multiple versions of the database are maintained concurrently to allow readers to execute without colliding with writers. Such a system allows a natural definition and implementation of such an isolation level. InterBase, later owned by Borland, was acknowledged to provide SI rather than full serializability in version 4, and likely permitted write-skew anomalies since its first release in 1985. Unfortunately, the ANSI SQL-92 standard was written with a lock-based database in mind, and hence is rather vague when applied to MVCC systems. Berenson et al. wrote a paper in 1995 critiquing the SQL standard, and cited snapshot isolation as an example of an isolation level that did not exhibit the standard anomalies described in the ANSI SQL-92 standard, yet still had anomalous behaviour when compared with serializable transactions. In 2008, Cahill et al. showed that write-skew anomalies could be prevented by detecting and aborting "dangerous" triplets of concurrent transactions. This implementation of serializability is well-suited to multiversion concurrency control databases, and has been adopted in PostgreSQL 9.1, where it is known as Serializable Snapshot Isolation (SSI). When used consistently, this eliminates the need for the above workarounds. The downside over snapshot isolation is an increase in aborted transactions. This can perform better or worse than snapshot isolation with the above workarounds, depending on workload.
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WS-SecurityPolicy

WS-Security Policy is a web services specification, created by IBM and 12 co-authors, that has become an OASIS standard as of version 1.2. It extends the fundamental security protocols specified by the WS-Security, WS-Trust and WS-Secure Conversation by offering mechanisms to represent the capabilities and requirements of web services as policies. Security policy assertions are based on the WS-Policy framework. Policy assertions can be used to require more generic security attributes like transport layer security , message level security or timestamps, and specific attributes like token types. Most policy assertion can be found in following categories: Protection assertions identify the elements of a message that are required to be signed, encrypted or existent. Token assertions specify allowed token formats (SAML, X509, Username etc.). Security binding assertions control basic security safeguards like transport and message level security, cryptographic algorithm suite and required timestamps. Supporting token assertions add functions like user sign-on using a username token. Policies can be used to drive development tools to generate code with certain capabilities, or may be used at runtime to negotiate the security aspects of web service communication. Policies may be attached to WSDL elements such as service, port, operation and message, as defined in WS Policy Attachment. == Sample Policies == Namespaces used by the following XML-snippets: ... Include a timestamp: Use either transport layer security (https) or message level security (XML Dsig/XML Enc): ... ... To define a SAML assertion as security token: ...#SAMLV2.0 Issued token assertion of providers with reference to the STS and required token format: http://sampleorg.com/sts http://docs.oasis-open.org/wss/oasis-wss-saml-token-profile-1.0#SAMLAssertionID ... ... Specify that message header and body need to be signed, and attachments are left unsigned: ? ... specify that message open source license need to be signed, and hydra security are left unsigned: ? ... == Other WS policy languages == The term Web Services Security Policy Language is used for two different XML-based languages: As described above, based on the WS-Policy framework, as defined in, published as version 1.3 in Feb. 2009 WSPL, based on XACML profile for Web-services, but that was not finalized.
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